online phd in data science europe

PhD Studies in Data Science

Phd offering.

The BSE Data Science Center is looking for students with a strong quantitative background interested in pursuing PhD studies in areas related to data science (DS): Statistics, Machine Learning, Probability, Operations Research, and their applications in Economics. To be eligible to apply to such PhD studies students should have (or be in the final year of) a degree in Data Science, Statistics, Mathematics or related discipline. A way to acquire such a background is to first enroll in our master in Data Science Methodology ( https://bse.eu/study/masters-programs/data-science-methodology ). To follow this route, you should indicate your interest in pursuing PhD studies in your motivation letter when applying to the master. Selected students receive a tuition waiver for the master program. We emphasize however that applying to a PhD is a separate process from enrolling into the Data Science master at BSE, and in particular that doing the master is no guarantee of being admitted into the PhD afterwards. Admittance to a PhD program is done on a competitive basis, and depends on the resources available each year. There are two main routes to pursue PhD studies. 1. Apply to the PhD program of the Dept. of Economics & Business at UPF. For students doing the master in Data Science, the application would be at the end of the 1st term of the program. Students should apply to the MRes year (Year 2) of the PhD program at UPF, see  https://www.upf.edu/web/econ/phd-track  for further information on how to apply. If admitted, upon entering the program, the student takes a selection of courses and produces a Master of Research thesis, which is typically preliminary work towards the PhD thesis. Data Science students take a specially designed coursework, selected from courses in  https://www.upf.edu/es/web/econ/courses , to be agreed upon with their advisors. 2. Apply to the PhD program at a collaborating institution, such as the Statistics program at the Universitat Politècnica de Catalunya ( https://www.eio.upc.edu/en/doctorate/doctoral-program-of-the-department-of-statistics-and-operations-research ). The PhD would be under the supervision of a Data Science Center Faculty member. In such a program there is typically no coursework and students start working on their PhD thesis from day 1.

How to apply

All interested applicants should submit the materials specified below to the Data Science PhD selection committee at  [email protected] , before Jan 15. Late applications may be considered in exceptional circumstances. 1. Students interested in the PhD program at UPF must also submit an application to Year 2, following the instructions for the MRes Online Application. The deadline for that application is usually Jan 15, but double-check the UPF website.   2. Students who wish to pursue a PhD at a collaborating institution outside UPF should, in a first instance, send their application to the Data Science PhD selection committee only.   Applications to the Data Science PhD selection committee must include: – A copy of your final official undergraduate academic transcript, showing courses taken and grades obtained. – If you have finished graduate studies or are currently undergoing a master’s degree when you submit your application, a copy of the final or provisional graduate academic transcript, showing courses taken and grades obtained – A motivation letter, including a concise statement on research interests. – Two academic reference letters. If applying to the UPF program, besides uploading the letters at the UPF system they should also be emailed to  [email protected] . Please ensure that your referees send the letters by the deadline

The Barcelona School of Economics Data Science Center coordinates and promotes interdisciplinary and methodological research, training, and knowledge transfer in Data Science. The Data Science Center community consists of leading academics, machine learning researchers from industry, and practitioners from the data science and analytics industry. The research group at the Data Science Center is leading in this area and has recently been recognized by several major funding bodies, for example, the BBVA grant in Big Data, and the Google Faculty Award.

The Data Science Center is part of the Barcelona School of Economics, which is a leading institution for research and graduate education in Economics and the social sciences. The BSE offers seven Master’s programs, including a Master’s in Data Science, coordinated by the Data Science Center.

The BSE was founded as an institution for scientific cooperation between four existing academic and research units with a long tradition of collaboration: Institut d’Anàlisi Econòmica, Centre de Recerca en Economia Internacional, Universitat Autònoma de Barcelona, and Universitat Pompeu Fabra. It continues to focus on consolidating strong research groups across these four centers, of which the Data Science Center is an example.

Universitat Pompeu Fabra (UPF) is a public, international and research-intensive university that, in just twenty-five years, has earned a place for itself among the best universities in Europe. Awarded with a CEI label (International Excellence Campus) by the Spanish Ministry of Education, the University also figures in some of the most influential rankings UPF has recently been featured as the 5th fastest-rising young university in the world by Times Higher Education, while the Department of Economics at the university is consistently ranked in the top 40 QS World University Rankings by Subject.

Contact Data Science Center Barcelona School of Economics Ramon Trías Fargas, 25-27 08005 Barcelona, Spain.

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Best Universities for Data Science in Europe

Updated: February 29, 2024

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• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
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Below is a list of best universities in Europe ranked based on their research performance in Data Science. A graph of 8.92M citations received by 333K academic papers made by 727 universities in Europe was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. University College London

For Data Science

2. University of Oxford

3. Imperial College London

4. University of Bristol

5. University of Manchester

6. University of Cambridge

7. University of Edinburgh

8. University of Southampton

9. University of Birmingham

10. King's College London

11. St George's, University of London

12. University of Sheffield

13. Catholic University of Leuven

14. University of York

15. University of Amsterdam

16. University of Glasgow

17. University of Liverpool

18. Swiss Federal Institute of Technology Zurich

19. University of London

20. Newcastle University

21. Leiden University

22. Delft University of Technology

23. University of Nottingham

24. University of Leeds

25. Federal Institute of Technology Lausanne

26. University of Zurich

27. Eindhoven University of Technology

28. University of Warwick

29. University of Leicester

30. Polytechnic University of Milan

31. Cardiff University

32. Technical University of Munich

33. University of Granada

34. Utrecht University

35. Uppsala University

36. Radboud University

37. Queen Mary University of London

38. University of Exeter

39. Technical University of Madrid

40. University of Bologna

41. Claude Bernard University Lyon 1

42. Vienna University of Technology

43. University of Copenhagen

44. Heidelberg University - Germany

45. Free University Amsterdam

46. Ghent University

47. University of Aberdeen

48. Technical University of Catalonia

49. University of Helsinki

50. University of Geneva

51. University of Pisa

52. RWTH Aachen University

53. University College Dublin

54. Sapienza University of Rome

55. Maastricht University

56. KTH Royal Institute of Technology

57. Lancaster University

58. Federico II University of Naples

59. Karlsruhe Institute of Technology

60. Polytechnic University of Valencia

61. University of Twente

62. Erasmus University Rotterdam

63. Norwegian University of Science and Technology

64. University of Bern

65. University of Stuttgart

66. London School of Economics and Political Science

67. University of Groningen

68. Polytechnic University of Bari

69. Aalto University

70. University of Porto

71. Technical University of Berlin

72. Ulster University

73. University of Wales

74. University of Patras

75. University of Oslo

76. Pierre and Marie Curie University

77. University of Dundee

78. Swansea University

79. Keele University

80. Aarhus University

81. National Technical University of Athens

82. Durham University

83. Lund University

84. University of Munich

85. City, University of London

86. Queen's University Belfast

87. Aalborg University

88. University of Surrey

89. University of Sussex

90. University of Vienna

91. University of Milan

92. University of Reading

93. Dresden University of Technology

94. Brunel University London

95. Wageningen University

96. University of East Anglia

97. Technical University of Denmark

98. University of Leipzig

99. Karolinska Institute

100. University of Padua

Computer Science subfields in Europe

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(in collaboration with scuola normale superiore, sant’anna school, university of pisa and national research council).

The Program

The PhD in Data Science is aimed at educating the new generation of researchers that combine their disciplinary competences with those of a “data scientist”, able to exploit data and models for advancing knowledge in their own disciplines, or across diverse disciplines. To this purpose, the PhD in Data Science develops a mix of knowledge and skills on the methods and technologies for the management of large, heterogeneous and complex data, for data sensing ( how to harvest data ), for data analysis and mining ( how to make sense of data ), for data visualization and storytelling ( how to narrate data ), for understanding the ethical issues and the social impact of Data Science. The PhD students will have the opportunity of developing data science projects in a variety of domains, including:

• Data science for society and policy
• Data science for economics and finance
• Data science for culture and the humanities
• Data science for industry and manufacturing
• Data science for biology and health
• Data science for the hard and environmental sciences
• Data science ethics and legal aspects
• Data science techniques and methods

The PhD leverages the critical mass of data science labs and researchers accumulated in Pisa since early 2000’s, across the University of Pisa, the ISTI and IIT institutes of the CNR (National Research Council), Scuola Normale Superiore, Sant’Anna School of Advanced Studies and the IMT School for Advanced Studies Lucca. These labs gave rise to pioneering European projects in big data analytics and data science, as well as to the earliest educational programs for data scientists at graduate and PhD level. In 2015, the European Commission has chosen this hub as the coordinator of the European Research Infrastructure for Big Data Analytics & Social Mining,  SoBigData     http://www.sobigdata.eu .  This initiative provides an ecosystem of data, analytics and competences to support inter-disciplinary open data science and data-driven innovation, within an ethical framework of transparency, privacy, and responsibility. SoBigData provides a unique platform for doctoral education in Data Science, recognized by the Ministry of Education, University and Research   [1] , where PhD students can carry out multi-disciplinary data-driven research.

The IMT School Representative for the Program is Prof. Rocco De Nicola .

   [1]  Rapporto MIUR BigData,    http://www.istruzione.it/allegati/2016/bigdata.pdf   pag. 33

For further information, please visit the   Program's website .

Teaching Activity

Teaching is articulated in two lines: alignment of data science skills, to create a common ground for students with diverse background, and applications of data science in disciplinary and multi-disciplinary contexts. For alignment, PhD students will have the opportunity to take selected courses offered by the post-graduate Master in “Big Data Analytics and Social Mining” (Master Big Data) of the University of Pisa, in collaboration with CNR, Scuola Normale Superiore, Sant’Anna School of Advanced Studies and SoBigData.eu. Available courses cover the basics of Data Science and Big Data Analytics:

• Big Data Sensing & Procurement (Analytical Web Crawling, Scraping, Web Search and Information Retrieval, Semantic Text Annotation, Big Data Sources, Crowdsensing)
• Big Data Mining (Data Mining, Machine Learning and Statistical Learning, Network Science and Social Network Analysis, Mobility Data Analysis, Web Mining, Nowcasting, Sentiment Analysis and Opinion Mining)
• Big Data Storytelling (Visualization, Visual analytics, Data Journalism)
• Big Data Ethics (Privacy-by-design, Data Protection Regulations, Responsible Data Science, Legal aspects of Data Science)
• Big Data Technologies (Data Management for Business Intelligence, High Performance & Scalable Analytics, NO-SQL Big Data Platforms).

A wide variety of PhD courses focusing on the multi-disciplinary applications of data science are offered by the participating institutions, also in synergy with existing disciplinary PhD programs. Students also have the opportunity to participate in summer schools organized in collaboration with international research institutions, and to the PhD+ program of the University of Pisa, for the development of entrepreneurial and innovation skills.

The PhD Board

Dino Pedreschi   (PhD Program Coordinator), University of Pisa Albert-Laszlo Barabasi , Northeastern University, Boston, USA Vincenzo Barone , Scuola Normale Superiore Roberta Bracciale , University of Pisa Chiara Cappelli , Scuola Normale Superiore Alessandro Cellerino , Scuola Normale Superiore Francesca Chiaromonte , Sant’Anna school of Advanced Studies Giulio Cimini , IMT School for Advanced Studies Lucca Marco Conti , National Research Council (CNR) Tommaso Cucinotta , Sant’Anna school of Advanced Studies Giuseppe De Pietro , National Research Council (CNR) Fabio Gadducci , University of Pisa Diego Garlaschelli , IMT School for Advanced Studies Fosca Giannotti , National Research Council (CNR) János Kertész , Central European University, Budapest Fabrizio Lillo , Università di Bologna Pietro Luigi Lopalco , University of Pisa Francesco Marcelloni , University of Pisa Stan Matwin , Dalhousie University, Halifax, CDN Anna Monreale , University of Pisa Elena Pavan , Scuola Normale Superiore Alex “Sandy” Pentland , MIT, USA Raffaele Perego , National Research Council (CNR) Andrea Piccaluga , Sant’Anna school of Advanced Studies Nadia Pisanti , University of Pisa Monica Pratesi , University of Pisa Chiara Maria Angela Roda , University of Pisa Salvatore Ruggieri , University of Pisa Tiziano Squartini , IMT School for Advanced Studies Lucca Franco Turini , University of Pisa

Call for applications

Details on upcoming and past calls for applications are available on the Program's website .

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Online Postgraduate Courses in Data Science in Europe - 35 Courses

University of aberdeen school of natural and computing sciences.

• Data Science (Online) MSc

Birkbeck, University of London School of Computing and Mathematical Sciences

• Applied Data Science Postgraduate Certificate - PgCert

The University of Edinburgh School of Informatics

• Data Science, Technology and Innovation MSc Postgraduate Certificate - PgCert Postgraduate Diploma - PgDip Professional Development Diploma
• High Performance Computing with Data Science (Online Learning) MSc Postgraduate Certificate - PgCert Professional Development Diploma

The University of Edinburgh School of Molecular, Genetic and Population Health Sciences

• Data Science for Health and Social Care (Online Learning) MSc Postgraduate Certificate - PgCert Postgraduate Diploma - PgDip Professional Development Diploma

LIBF Data Science

• Data Science Master of Science - MSc (PG)

Keele University School of Computing and Mathematics

• Computer Science with Data Analytics (Online) MSc
• Mc Computer Science with Data Analytics - 100% Online MSc

University of Liverpool Online Programmes

• Data Science and Artificial Intelligence Online MSc

Queen Mary University of London Barts Cancer Institute

• Cancer Genomics and Data Science (online) MSc

University of St Andrews Computer Science

• Data Science - online MSc Postgraduate Certificate - PgCert Postgraduate Diploma - PgDip

University of Strathclyde Faculty of Science

• Applied Statistics with Data Science (online) MSc

University of Bristol Computer Science

Edinburgh napier university school of accounting, financial services and law.

• Data Science MSc

Glasgow Caledonian University Engineering

• Applied Data Science in Engineering MSc

University of Hertfordshire Business Analysis and Statistics

• Data Science and Analytics (Online) MSc

Imperial College London Mathematics

• Machine Learning and Data Science (Online) MSc

International University of Applied Sciences Single Tier Structure

• Data Management MSc
• MBA in Big Data Management MBA

University of Leeds Digital Education Service

• Data Science (Statistics) MSc

University of Liverpool Computer Science

• Data Science and Artificial Intelligence (Online) MSc

Northumbria University Computer and Information Sciences

• Computer Science with Data Analytics MSc
• Information Science (Data Analytics) MSc

University of Wales Trinity Saint David Computing

• Data Science and Analytics Master of Science - MSc (PG)

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PhD in Computing, Data and Artificial Intelligence

The PhD theses conducted within the domain Computer science, data & AI of the Doctoral School of Institut Polytechnique de Paris aim at advancing the state of the art in the whole domain, starting from the most fundamental questions of computer science, related to the efficient storage and the fast processing of massive data, up to the most complex systems, like cyber-physical systems or sets of machines able to understand their environment, to interact between them and with humans, to take decisions in an autonomous way and to explain them.

The PhD works address in particular questions about:

• Computer architectures, programming languages, compilation, formal methods and proofs
• Quantum information science
• High performance computing
• Virtualization
• Cloud computing
• Data mining
• Representation of knowledge
• Operational research
• Optimization techniques
• Learning and processing techniques for images, videos, audios, texts
• Distributed systems, embedded systems, real-time systems
• Communication networks
• Internet of things,
• Robotics, autonomous systems
• Human-machine interaction
• Virtual / augmented reality
• Ddata visualisation,
• Social networks
• Cyber-security
• Control and protection of personal data.

These works have a strong technological impact thanks to the rich industrial environment of Institut Polytechnique de Paris.

• Computer Science
• Signal and Image Processing
• Automatics and Robotics
• Ecole Polytechnique
• ENSTA Paris
• Telecom Paris
• Telecom SudParis

Deep learning for multi-temporal analysis of remote sensing images, Rodrigo Daudt (Télécom Paris)

Human gesture recognition, Chuang Yu (ENSTA Paris)

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• Our France Campus Our campus is located in Paris and various programs from 1 to 3 years in duration are offered. Our programs are very unique with paid internships, multiple certifications, and job opportunities.

Doctorate of Business Administration – Data Science

Batch starts on 1st day of each month.

Apply before 25th of the Month

Program Length

Total Months

Total ECTS Credits

Program fee includes tuition and all other applicable fees, currency calculator.

ESDST’S Doctorate of Business Administration in Data Science focuses upon creating business leaders with an exceptional data-backed decision-making capabilities. The program aims at broadening mindset to review data set and recommend meaningful solutions for the given scenario. 

The program involves generating awareness on every step of statistical analysis which include cleaning the data, assessing reliability and validity of the data. This program specifically prepares you on designing and execute a research methodology which is quantitative in nature to solve a real business problem. 

The key distinction in our DBA in Data Analytics is to learn from practitioners at every stage which enables you to learn a more practical approach in gathering new information, process complex data and analyse the same to suggest business solutions. The program follows a practitioner mentor – research scholar approach wherein each research scholar is assigned a mentor who is practising in the industry. 

Highlights:  

• Scope and applications of data analytics to solve wider level business problems
• Engaging online courses let you schedule your learning around your work and your lifestyle
• Constant guidance and support by industry mentors
• Live projects with real life datasets and complex business problems
• Prevalent business problems and application of data analysis to deliver a solution
• Learn to apply theoretical concepts to solve business problems
• Keeps you updated on the latest industry trends
• Value added real-life guidance and project discussion with the industry experts
• Many multinational companies are involved in delivering, mentoring and support
• Dissertation on solving real business problem with systematic analysis of data                                                                                                                                                                                                           

Approx. Course Length

2-3 5-7 weeks

Max. Number of Transfer Credits:

DBA- Data Science is planned to be delivered 100% online, with all material and resources included and provided through ESDST e-campus. 

The program comprises of 6 modules per year for 2 years along with the value-added seminars and the written assignments accompanying each module. The aim of these modules is to empower scholars on designing data-driven research methods and analysis which is good for the business and society at large. The taught modules contain research modules, domain-specific modules like Data Science or data science, etc, value-added seminars.

In addition to the taught modules, scholars are guided continually through five residencies starting from Year 1. The students’ residencies can be conducted offline or online depending upon the number of participants and their locations.

The writing targets for scholars to produce their research output in terms of publications, writing research proposals, carry data collection, teaching initiatives, etc. are also the main learning vehicle for the scholars. These are the tangible outcomes scholars produce and attain skills necessitated to conduct real research and corresponding recommendations for business problems.

CAREERS & OUTCOMES

ESDST DBA-Data Science is a recognised degree which is delivered 100% online, with a possibility to visit campus for Residency programs. It provides you flexibility to pursue your work and integrate learnings from your DBA immediately to solve real business problem you company might be facing. This andragogy enables you to implement the learnings and reflect critically upon business scenarios which is essential for continuous learning. The assignments and report writings are also one of the learning vehicles which help developing skill-sets such as communication skills, critical cognition, etc. imperative for one’s professional growth.

Practitioner Mentor – Research Scholar approach is specially designed to allow our scholars learn from mentors who are engaged in solving real-world business problems. You being a practitioner is studying from Professors and practitioners from around the world. This provides the best combination for reflective higher-order learning. The residency workshops allow you to consolidate your learnings in individual modules and guides you to prepare tangible outputs. The writing targets enhances your skill-set, which prepares you to be best in the industry. 

Primary   outcomes:

• Scan business environment and identify data employment for making business decisions.
• Considering environmental and ethical factors, prepare research design to analyze complex, real-world problems.
• Develop technical communication skills to communicate the results effectively through utilizing Data Visualization Tools.
• Cultivates values and attitudes that make participants agents of critical change and performance upliftment
• Comprehend information analytically through the process of research and inquiry while making effective decisions in global environment
• Build the positive perspectives and skills that create productive managerial leaders and business networks to create world class teams.

After completing the program, the career path will be influenced by what stage of cognition the student is. For working professional students, the positive impact of the programme happens from the first day since the student can apply the knowledge and skills right away at work. Students can target any of the following roles:

• Chief Executive Officer
• Business Analytics Manager
• Data Manager

ESDST maintains very high standards for students who enter our academic programs. For entry into ESDST DBA programs the following criteria need to be met before admission is offered to a prospective student.

Qualifications:

Master’s Degree or an equivalent recognized academic title in any discipline.

Qualifications Waiver:

For students who don’t have a Master’s Degree, ESDST uses Recognition of Prior Experience (RPE). ESDST generally utilizes over 3 years of relevant experience in a significant role for waiver of the course work. Please contact us at [email protected] for more details.

Proficiency in English: Evidence of Proficiency in English IELTS 6.0+, OR PTE 50+, OR TOEFL 550+, OR Any other proficiency test taken in the last 2 years

English Proficiency Waiver: The English proficiency test is waived for the following candidates: Native English Speakers,  OR ; Applicants having completed their schooling in English (i.e. High School Diploma or IB), OR; Applicants having completed their undergraduate degree in English in an English speaking country, OR; 2 Years of work experience in a setting in which English is the primary language of work

ACCREDITATION COUNCIL FOR BUSINESS SCHOOLS AND PROGRAMS (ACBSP)

Through its parent institution, Rushford Business School, ESDST is a member of the “Accreditation Council for Business Schools and Programs (ACBSP). ACBSP is a global accrediting body that accredits business programs at the associate, baccalaureate, and graduate degree levels worldwide since 1988. Rushford Business School is part of a membership that extends to more than 60 countries. ACBSP members are amongst the best educators in their respective fields, interested in learning innovative teaching methods, improving the delivery of business education programs, and creative value for their students.

INTERNATIONAL ACCREDITATION COUNCIL FOR BUSINESS EDUCATION (IACBE)

ESDST through its parent schools Rushford Business School and James Lind Institute is a member of the “International Accreditation Council for Business Education (IACBE)” The IACBE accredits business programs that lead to degrees at the associate, bachelor’s, master’s, and doctoral levels in institutions of higher education worldwide. All modes of delivery, campuses, locations, and instructional sites, as well as all business programs regardless of degree level, will normally be included in the IACBE accreditation review.

UNITED NATIONS PRINCIPLES FOR RESPONSIBLE MANAGEMENT EDUCATION (PRME)

ESDST through Rushford Business School is a proud supporter and Signatory of the United Nations Principles for Responsible Management Education (UN PRME). PRME is an initiative of the United Nations Global Compact founded in 2007 as a platform to encourage and increase awareness and integration of sustainability in business schools around the world. Today, PRME is the largest coordinated effort between the world’s business schools and the United Nations. Rushford Business School became a PRME signatory in 2020. As a school, we understand the privilege and responsibility of providing quality education that gives learners the knowledge and tools they need to succeed, change lives, and transform societies.

Swiss Higher Educational Institution

James Lind Institute is an approved post-secondary higher educational Institution with the authority to award private degrees in Switzerland. The institute is registered in the Canton of Geneva, Switzerland under the UID CHE-255.747.977.

International Council For Open & Distance Education (ICDE), Norway

ESDST through its parent institution James Lind Institute is a proud member of the prestigious International Council for Open & Distance Education. ICDE has consultative partner status with UNESCO and shares UNESCO’s key value – the universal right to education for all. ICDE further derives its position from the unique knowledge and experience of its members throughout the world in the development and use of new methodologies and emerging technologies.

International Organization For Standardization (ISO) 9001:2015 Certified

James Lind Institute (JLI) is fully accredited by the AMERICAN BOARD OF ACCREDITATION SERVICES (ABAS) as per ISO 9001:2015 standards for providing Training & Education Programs related to healthcare and allied sectors.

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PhD in Data Science and Computation

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PhD Programme in Data Science and Computation

Andrea cavalli.

Dipartimento di Farmacia e Biotecnologie - FaBiT

Via Belmeloro 6 Bologna (BO)

[email protected]

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Data Science Prof Doc

This course is in clearing with spaces available

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The Professional Doctorate in Data Science (D.DataSc) is aimed at professionals who wish to enhance and/or validate data-centric, evidence-based approaches within their chosen career through a combination of taught modules and doctoral research.

The programme is delivered:

• Full-time, three years: one year of taught modules and two years of research
• Part-time, five years:  two years of taught modules and three years of research

A cross-disciplinary approach is central to the delivery of this programme and is therefore suitable for professionals in a broad range of professional disciplines and areas of employment.

"The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that's going to be a hugely important skill in the next decades." (Hal Varian, Chief Economist at Google).

The programme is unique, international, and ground-breaking in offering a Professional Doctorate qualification in Data Science. D.DataSc is an earned doctorate that allows the holder to use the title 'Dr'.

This course is only eligible for part-time student visa sponsorship. For more details about the restrictions of part time student visas please see our Student Visa page .

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Course options

• September 2024

Professional Doctorate

Entry requirements, academic requirements, accepted qualifications.

Bachelor's degree with Upper Second Class (2:1) in Physical Science, Electrical, Electronic, Communication Engineering or Humanities and Social Science related subject.

International Qualifications

We accept a wide range of European and international qualifications in addition to A-levels, the International Baccalaureate and BTEC qualifications. Please visit our International page for full details.

English Language requirements

Overall IELTS 6.5 with a minimum of 6.0 in Writing, Speaking, Reading and Listening (or recognised equivalent). If you do not meet the academic English language requirements for your course, you may be eligible to enrol onto a pre-sessional English course .

The length of the course will depend on your current level of English and the requirements for your degree programme. We offer a 5-week and an 10-week pre-sessional course.

Mature applicants and those without formal qualifications

As an inclusive university, we recognise those who have been out of education for some time may not have the formal qualifications usually required. We welcome applications from those who can demonstrate their enthusiasm and commitment to study and have the relevant life/work experience that equips them to succeed on the course. We will assess this from the information provided in your application or may request additional information such as a CV or attendance at an interview. Please note that some courses require applicants to meet the entry requirements outlined.

Admissions policy / Terms of Admittance

We are committed to fair admissions and access by recruiting students regardless of their social, cultural or economic background. Our admissions policy sets out the principles and procedures we use to admit new students for all courses offered by the university and its partners.

Further advice and guidance

You can speak to a member of our Applicant Enquiries team on +44 (0)20 8223 3333, Monday to Friday from 9am to 5pm. Alternatively, you can visit our Information, Advice and Guidance centre.

Prof Doc Data Science

Prof doc data science, home applicant, full time.

• Home Applicant
• Full time, 3 years
• 10200 First year fees £10,200 (taught element), then £6,020 per year for the next two research years. Pound 10200 First year fees £10,200 (taught element), then £6,020 per year for the next two research years.

Prof Doc Data Science, home applicant, part time

• Part time, 5 years
• 1700 First year fees £1,700 (taught element) per 30 credit module, then £3,010 per year for the next three research years. Pound 1700 First year fees £1,700 (taught element) per 30 credit module, then £3,010 per year for the next three research years.

Prof Doc Data Science, international applicant, full time

• International Applicant
• 15960 First year fees £15,960 (taught element), then £16,100 per year for the next two research years. Pound 15960 First year fees £15,960 (taught element), then £16,100 per year for the next two research years.

Prof Doc Data Science, international applicant, part time

• 2660 First year fees £2,660 (taught element) per 30 credit module, then £8,050 per year for the next three research years. Pound 2660 First year fees £2,660 (taught element) per 30 credit module, then £8,050 per year for the next three research years.

Fees, funding and additional costs

EU, EEA and Swiss Nationals starting a course from September 2021, will no longer be eligible for Home fees. However, such nationals benefitting from Settled Status or Citizens' Rights may become eligible for Home fees as and when the UK Government confirms any new fee regulations.  Further information can be found at UKCISA .

Tuition fees are subject to annual change. Fees for future years will be published in due course.

Home students

Postgraduate loans scheme.

£10,280 to fund your Masters Programme under the Postgraduate Loans (PGL) scheme

Postgraduate Loans (PGL)

The Postgraduate Loan (PGL) provide non-means-tested loans of up to £10,906 to taught and research masters students.  It will be paid to students as a contribution towards tuition fees, living costs and other course costs. Applications are made directly through  Student Finance England  

Eligibility

Whether you qualify depends on: •    if you've studied a postgraduate course before •    your course •    your age •    your nationality or residency status

Full eligibility can be found on the Government's Postgraduate Loan webpage .

Please take a look at the  Postgraduate Loans  for an overview of the new funding.

Postgraduate Scholarship

Apply for a 50 per cent discount on your tuition fees! You can get a 50 per cent discount on course fees through a UEL Postgraduate Scholarship. The scholarship is open to full-time and part-time UK and EU students of taught postgraduate courses. *Exclusions apply.

Find out more about full eligibility criteria and how to apply .

Terms and conditions apply.

Our scholarships and bursaries can help you

How we can help you

Did you know that with a postgraduate qualification, you can expect to earn more than someone who only holds an undergraduate degree?

If you want to build new skills, change career paths, or further your career prospects, a postgraduate degree can help you. Our range of scholarships and bursaries will make financing your education that much easier. Below is some of the funding available to support you in your studies:

• Alumni Discount   - up to 15% fee waiver *exclusions apply. Please see the Alumni Discount page  for information.
• Early Payment Discount  - 5% fee waiver
• Asylum Seekers scholarship   - 100% fee waiver
• Civic Engagement - £1,000
• Hardship Bursary - up to £2,000
• Sport Scholarships   - Up to £6,000

How to pay your fees

There are a number of ways you can pay your fees to UEL

• Online payment facilities
• By telephone
• In person at our Docklands or Stratford campus
• Bank transfer

Full information on making payments can be found  on our Finance page

If you wish to discuss payments to the University, please contact our Income Team on 020 8223 2974 or you can email  [email protected]

Ideas for funding your postgraduate study

Below are some ideas on how to fund your postgraduate study:

•     Apply for a  Postgraduate Loan  
•     Take advantage of  UEL scholarships and bursaries
•     Ask your employer to sponsor your study
•     Study part-time so you can work at the same time (applicable to courses that have a part-time mode)
•     Look at  UK Research and Innovation funding options

The Student Money Advice and Rights Team (SMART) are here to help you navigate your finances while you're a student at the University of East London. We can give you advice, information and guidance on government and university funds so that you receive your full funding entitlement. Live chat: Click the live chat icon in the bottom left of the screen Phone: 020 8223 4444

International students

Living costs for international students.

As part of the Tier 4 student visa requirements, UK Visas and Immigration (UKVI) estimate that you will need £1,265* per month to cover your living costs. It includes expenses for accommodation, food and drink, travel within London, textbooks, entertainment, clothing, toiletries and laundry. Most Tier 4 students are required to show they have sufficient funds to cover the first nine months of the course before they start - a total of £11,385 - in addition to the tuition fees. You can find more information about the specific requirements of the Tier 4 student visa. The amount that you will spend can vary depending on your lifestyle. The UKCISA International Student Calculator can help you plan and manage your money.

* Please note the Immigration Rules are subject to change and this figure is likely to be increased by UKVI year on year. Please therefore check our ISA page for more information at the time of preparing your visa application.

How to pay your fees - international students

Deposits and paying by instalments International students are required to pay a  deposit  before being issued a Confirmation of Acceptance for Studies (CAS). Your remaining balance will be paid in five monthly instalments over your first term. The first of these instalments must be paid when completing your enrolment on arrival at UEL. Please follow the payment instructions on our Make a Payment page . After the required payment has been made, you will be asked to complete the online International Student Reply Form to confirm your acceptance of our offer and of our terms of admittance and fee policy.

Our International team at UEL are available for advice and guidance on studying in London, fees, scholarships and visa requirements. Email:  [email protected]

Additional costs

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. Accommodation and living costs are not included in our fees. 

Our libraries are a valuable resource with an extensive collection of books and journals as well as first-class facilities and IT equipment. You may prefer to, or be required to, buy your own copy of key textbooks.

Computer equipment

There are open-access networked computers available across the University, plus laptops available to loan. You may find it useful to have your own PC, laptop or tablet which you can use around campus and in halls of residences.

Free WiFi is available on each of our campuses.

In the majority of cases, coursework can be submitted online. There may be instances when you will be required to submit work in a printed format. Printing and photocopying costs are not included in your tuition fees.

Travel costs are not included but we do have a free intersite bus service which links the campuses and halls of residence.

For this course, you will be:

• involved in processes of making, as a means of exploration, experimentation, and understanding your practice, by using a diverse range of media and materials
• required to purchase your own copy of books, for required reading
• required to produce physical artefacts for assessment 
• able to participate in optional study visits and/or field trips

However, over and above this you may incur extra costs associated with your studies, which you will need to plan for. 

In order to help you budget, the information below indicates what activities and materials are not covered by your tuition fees:

• personal laptops and other personal devices 
• personal copies of books 
• optional study visits and field trips (and any associated visa costs)
• printing costs
• your own chosen materials and equipment
• costs of participating in external events, exhibitions, performances etc.

The costs vary every year and with every student, according to the intentions for the type of work they wish to do. Attainment at assessment is not dependent upon the costs of materials chosen.

Learn about applying

Important information about your application, uk full-time starting sept.

How to Apply Apply directly to UEL by clicking on the apply button. For further information read our  Guide to Applying . When to Apply Places on many courses are limited and allocated on a first come first served basis. We advise you to apply as early as possible to give yourself the best chance of receiving an offer. Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone. +44 (0)20 8223 4354 Already applied? You can track the progress of your application by contacting our Applicant Engagement team on +44 (0)20 8223 3333 (Monday - Friday, 9am -5pm). Read our  guide to applying  for further information. Need help? Contact our Applicant Engagement team (Monday - Friday, 9am-5pm) +44 (0)20 8223 3333

UK Part-time starting Sept

How to Apply Apply directly to UEL by clicking on the apply button. For further information read our  Guide to Applying . When to Apply Places on many courses are limited and allocated on a first come first served basis. We advise you to apply as early as possible to give yourself the best chance of receiving an offer. Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone. +44 (0)20 8223 4354 Already applied? You can track the progress of your application by contacting our Applicant Engagement team on +44 (0)20 8223 3333 (Monday - Friday, 9am -5pm). Read our  guide to applying  for further information. Need help? Contact our applicant engagement team (Monday - Friday, 9am-5pm) +44 (0)20 8223 3333

International Full-time starting Sept

Submitting your application please read and consider the entry and visa requirements for this course before you submit your application. for more information please visit our  international student advice pages .  .

How to Apply We accept direct applications for international students. The easiest way to apply is directly to UEL by clicking on the red apply button. Please be sure to  watch our videos  on the application process.

When to Apply Please ensure that you refer to the international admissions deadline . We advise you to apply as early as possible to give yourself the best chance of receiving an offer.

International students who reside overseas Please ensure that you have read and considered the entry requirements for this course before you submit your application. Our enquiries team can provide advice if you are unsure if you are qualified for entry or have any other questions. Please be sure to read about the  Tier 4 visa requirements .

Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone.

+44 (0)20 8223 4354 Need help? Contact our applicant engagement team (Monday - Friday, 9am-5pm)

+44 (0)20 8223 3333

About our foundation years

Our Foundation Year courses are perfect for you if you... 

• are returning to education after a long time, or you don't have the qualifications for direct entry into our degree programmes
• are thinking of re-training and would like an introduction to the area
• are an international student wanting an additional year to adapt to the UK academic system
• are still evaluating which degree pathway at UEL is the right one for you

Please note: Foundation years can only be studied full time. However you can transfer to part-time delivery once you have completed your foundation year. Please apply to the full-time option if you wish to study in this way.

What makes this course different

Professional skill development

Block mode teaching, suitable for students in employment, allowing for professional skill development.

Enhanced knowledge

Integration of concepts, techniques and applications to enhance students' knowledge and skills in the analytics pipeline.

Open Source software tools

Open Source software tools which are widely used in the field of Data Science to extract value from data.

Course modules

Mental wealth; professional life (data ecology).

This module aims to develop a critical understanding of the world of data and Data Science from an ‘ecological’ perspective. This will focus on an understanding the environment of production, dissemination, harvesting and use of data in the data value chain as well as the development of niche areas from a perspective of evolution, competition, life cycle, cross-fertilisation and the niche space. This module focuses on many aspects of working in an Industry 4.0 economy.

Research Methods for Technologists

Applied research tools and techniques, work-based project review, planning for doctoral research, advanced decision making: predictive analytics & machine learning.

This module aims to develop a deep understanding of ways of making decisions that are based strongly on data and information. Particular focus will be on mathematical, statistical and algorithmic-based decision-making models using predictive analytics and machine learning. Various cases will be examined. The software environment will be predominantly open-source.

Spatial Data Analysis

This module aims for students to understand the concept and theory of spatial data analysis, and develop the skill and problem-solving ability by applying a range of spatial query, processing, visualisation and analysis techniques. Main platforms with be open source SpatiaLite and QGIS.

NOTE: Modules are subject to change. For those studying part time courses the modules may vary.

Download course specification

PDF, 185.2kb

What we're researching

Data analysis, data mining and modelling, Geocomputation and mapping, and data management. Professor Brimicombe is Emeritus Professor at UEL. He is a Chartered Geographer, an Academician of the Academy of Social Sciences, a Fellow of the Royal Statistical Society, a fellow of Royal Geographical Society, deputy chair of the National Statistician's Crime Statistics Advisory Committee and a non-executive committee member of the British Society of Criminology. He has been a Specialist Advisor to the House of Lords. Allan's expertise focuses around cross-disciplinary applications of Geo-Information Science and Data Science. Allan pioneered the use of geo-information systems and environmental simulation modelling. His other research interests include: data quality issues, spatial data mining and analysis, predictive analytics and location-based services (LBS). These have been applied to crime, health, education, natural hazards, utilities and business. Allan's recent projects include Olympic Games Impact Studies and Smart City Studies. Dr Yang Li is a fellow of the Royal Geographical Society, a fellow of the Royal Statistical Society, a fellow of the Higher Education Academy and a member of the Association of Geographic Information. Yang has rich experiences in both applications and research of Data Science and Geo-Information Science. He has expertise in data integration, data mining and data modelling. Particularly, he is a specialist in geocomputational analysis including data quality modelling and sensitivity analysis. Yang's recent projects include Olympic Games Impact Studies, the Prevent Project of the Home Office and TURaS.

Your future career

This programme uniquely qualifies students in a field increasingly recognised as central to most professional areas and research. The research component provides a solid grounding in methods and engagement with leading-edge ideas. Job opportunities in data science are rising exponentially. Holders of a Professional Doctorate in Data Science will have the highest possible qualification in this area and prepare them for senior positions. They will also be eligible to apply for Royal Statistical Society membership.

Our students are professionals from a diverse range of areas. They include a global compliance engineer, a senior system analyst, an analytical chemist, an assistant dean at Qatar University, a SAP technology consultant from Germany, an IT trainer, a senior project manager with Diageo, an ICT manager from Ireland, a lecturer in databases from Oman, a principal consultant with Verizon, a company MD, a senior analytical consultant with TripAdvisor, a consultant with HSBC,  a software developer with HMRC, a school teacher, a marketing officer,  a data manager in Microsoft and a data analyst from New York. 

All are looking to improve their career options and general expertise in this expanding market.

Explore the different career options you can pursue with this degree and see the median salaries of the sector on our  Career Coach portal .

How we support your career ambitions

We offer dedicated careers support, further opportunities to thrive, such as volunteering and industry networking. our courses are created in collaboration with employers and industry to ensure they accurately reflect the real-life practices of your future career and provide you with the essential skills needed. You can focus on building interpersonal skills through group work and benefit from our investment in the latest cutting edge technologies and facilities.

Career Zone

Our dedicated and award-winning team provide you with careers and employability resources, including:

• Online jobs board for internships, placements, graduate opportunities, flexible part-time work.
• Mentoring programmes for insight with industry experts 
• 1-2-1 career coaching services 
• Careers workshops and employer events 
• Learning pathways to gain new skills and industry insight

Mental Wealth programme

Our Professional Fitness and Mental Wealth programme which issues you with a Careers Passport to track the skills you’ve mastered. Some of these are externally validated by corporations like Amazon and Microsoft.

We are careers first

Our teaching methods and geographical location put us right up top

• Enterprise and Entrepreneurship support 
• We are ranked 6th for graduate start-ups 
• Networking and visits to leading organisations 
• Support in starting a new business, freelancing and self-employment 
• London on our doorstep

What you'll learn

Our doctoral research course focuses on pure or applied aspects of data science, with each student studying data from within their main discipline or area of employment. You will learn reflective and analytic approaches to data while engaging in your own data research.

The taught elements of the course include Data Ecology, Research Methods for Technologists, Applied Research Tools and Techniques, Spatial Data Analysis, Advanced Decision Making, Work-based Project Reviews and Planning for Doctoral Research.

These elements will be reinforced by the specialist knowledge of our course leaders, whose fields of expertise include data cleansing, data integration, data mining, spatial analysis and predictive analytics.

Their recent research has engaged them in data from crime statistics, natural hazards, public health and business, keeping them at the forefront of new developments in the field.

Our cross-disciplinary approach to the subject means that whatever your area of interest, our researchers will have the experience and expertise to enhance your knowledge and skills.

The taught modules on this course are available to be taken as credit-bearing short courses by suitably qualified individuals.

How you'll learn

This programme includes six taught modules and a Research Thesis and is available in full-time and part-time modes. Delivery of taught modules is by block and blended learning.

For those studying full-time, there are two years of research and for those studying part-time,  it is two years of taught modules and three  years of research.

Each taught module is based on one week's intensive attendance at the Docklands campus, according to an advertised calendar, usually at the beginning of each semester. Students are expected to have a laptop computer for in-class practical sessions. During the remainder of the semester, students can work on their reading, practical components (from a workbook) and coursework. Students will be supported online or on campus depending on individual students' arrangements. The taught modules on this programme are available to be taken as credit-bearing short courses by suitably qualified individuals.

How you will be assessed

All the learning outcomes of the programme are assessed through:

• Laboratory session portfolios
• Research thesis

Campus and facilities

Our campus and the surrounding area.

Our waterfront campus in the historic Royal Docks provides a modern, well-equipped learning environment.

Join us and you'll be able to make the most of our facilities including contemporary lecture theatres and seminar rooms, art studios and exhibition spaces, audio and visual labs and a multimedia production centre.

Features include our 24/7 Docklands library, our £21m SportsDock centre, a campus shop and bookstore, the Children's Garden Nursery, cafés, eateries, a late bar, plus Student Union facilities, including a student lounge.   University of East London is one of the few London universities to provide on campus accommodation. Our Docklands Campus Student Village houses close to 1,200 students from around the world. We are well connected to central London and London City Airport is just across the water. We also run a free bus service that connects Docklands with Stratford campuses.

Who teaches this course

This course is delivered by the School of Architecture, Computing and Engineering.

The teaching team includes qualified academics, practitioners and industry experts as guest speakers. Full details of the academics will be provided in the student handbook and module guides.

Related courses

This course is part of the Computer Science and Digital Technologies subject area.

Prof Doc Information Security

This programme aims to develop research-based practice amongst professionals currently working within the Information Security area.

Architecture, Computing and Engineering MPhil PhD

ACE has strong research expertise in urban sustainability, cyber-security and big data studies. We're world leaders in environmental protection studies.

TERMS AND CONDITIONS Modal

Terms of Admittance to the University of East London

The Terms of Admittance govern your contractual relationship with University of East London ("UEL"). A contract between you, the Student, and us, UEL, is entered into once you accept an offer of a place on a programme at UEL and this contract is subject to consumer protection legislation. You are entitled to cancel this contract within 14 days of enrolment onto your programme.

1) Student enrolment

Enrolment at UEL is the process whereby you officially become a UEL student. The enrolment process requires you to:

• Ensure that we are holding correct personal details for you
• Agree to abide by our regulations and policies
• Pay your tuition fees/confirm who is paying your tuition fees

You are expected to enrol by the first day of your academic year (click on "Discover") which will be notified to you in your enrolment instructions. Failure to enrol by the deadline contained in our Fees Policy (for most students by the end of the second week of teaching) may lead to the cancellation of student status and all rights attached to that status, including attendance and use of UEL's facilities. If you do not complete the formal process of enrolment but, by your actions, are deemed to be undertaking activities compatible with the status of an enrolled student, UEL will formally enrol you and charge the relevant tuition fee. Such activities would include attendance in classes, use of online learning materials, submission of work and frequent use of a student ID card to gain access to university buildings and facilities. Late enrolment charges may be applied if you do not complete your enrolment by the relevant deadline.

2) Tuition fees

Your tuition fee is determined by:

• the programme you are studying;
• if you are studying full or part-time;
• whether you are a UK/EU or International student; and when you started your studies with us.

We will tell you the tuition fee that you are due to pay when we send you an offer as well as confirming any additional costs that will be incurred, such as bench fees or exceptional overseas study trips. Unregulated tuition fees (where the UK government has not set a maximum fee to be charged) are generally charged annually and may increase each year you are on the programme. Any annual increase will be limited to a maximum of 5% of the previous year's fee. Regulated tuition fees (where the UK government has set a maximum fee to be charged) may also be subject to an annual increase. Any annual increase will be in line with the increase determined by the UK government. You will be notified of any increases in tuition fees at re-enrolment onto the programme. Further information on tuition fees and payment options are contained in our Fees Policy .

3) Student ID Cards

To produce an ID card, we need a recent photograph of you that is not obscured and is a true likeness. We will either ask you to send us/upload a photograph in advance of enrolment or take one of you at the point of enrolment. The photograph will be held on our student records system for identification purposes by administrative, academic and security/reception staff. By accepting these Terms of Admittance you are confirming that you agree to your photograph being used in this way. If you object to your photograph being used in this way please contact the University Secretary via email at gov&[email protected] . You are required to provide proof of your identity at initial enrolment and prior to the issue of your UEL student ID card. This is usually a full and valid passport but instead of this you may bring two of the following:

• A (full or provisional) driving licence showing current address
• An international driving licence
• An original birth certificate (in English)
• A debit or credit card (one only)
• A benefit book or benefit award letter (dated within the last 3 months)
• An Armed Forces Identity card
• A police warrant card

You are required to carry and display your student ID card whilst on UEL premises and must keep it safe so that it is not misused by others.

4) Proof of qualifications

You are required to produce evidence of having satisfied the entry requirements for your programme. Such evidence must be in the form of the original certificates or certified notification of results from the examining body. All qualifications must be in English or supported by an official certified translation. If you fail to provide evidence of having satisfied the requirements for the programme you are liable to be withdrawn from the programme.

5) Non-academic entry requirements

You may need to demonstrate that you have met non-academic entry requirements prior to enrolment by providing additional information to UEL. For example, if you:-

• are under 18 years of age at the time of initial enrolment,
• are applying to a programme that requires health clearance for study as stated in the programme specification,
• have declared a relevant criminal conviction,
• will be studying a programme that involves contact with children and/or vulnerable adults or leads to membership of a professional body that deals with children and/or vulnerable adults.

You will not be permitted to enrol and any offer will be withdrawn if UEL deems that you are unsuitable for study following assessment of this additional information in line with published policies. These policies will be provided to you when the additional information is requested.

6) Criminal convictions

UEL has a responsibility to safeguard staff, students and the wider community. You are required to inform UEL of any relevant criminal conviction you have and provide further information relating to these as requested. This includes any relevant criminal convictions received whilst studying at UEL. UEL will assess all information received in line with published policies and may remove you from a programme if the conviction makes you unsuitable for study in UEL's opinion. Failure to declare a relevant criminal conviction or provide further information about you may result in expulsion from UEL.

7) Providing false information to UEL

If you are discovered to have falsified or misrepresented information presented to UEL at application, enrolment or during your studies, you may be expelled from UEL.

8) Continued enrolment and student status

You are expected to abide by all UEL policies and regulations, both those in force at the time of first and subsequent enrolment and as later revised and published from time to time. UEL reserves the right to make reasonable changes to its policies and regulations and any substantial amendments will be brought to your attention. You are also required to take personal responsibility for your studies; this includes undertaking all study in support of your programme as prescribed by UEL. Key policies include: Manual of General Regulations This describes the general regulatory framework of UEL and gives information about how UEL confers its degrees, diplomas and certificates. It includes important information about academic performance requirements for continued study. Engagement Attendance Policy This outlines UEL's expectations of students in relation to attendance on and engagement with taught programmes. These students are expected to attend all scheduled classes and engage fully with learning materials and resources provided to them - failure to do so may result in withdrawal from module(s) and/or the programme. Code of Practice for Postgraduate Research Degrees The purpose of this code is to provide a framework for the successful organisation and implementation of good practice in all matters relating to postgraduate research degrees at UEL. It aims to ensure that all students are effectively supported and supervised so that the full scope and potential of their research is realised; that their thesis is submitted within regulatory periods and that they complete their programme with a suitable and sufficient portfolio of research and employment-related skills and competencies. Health and Safety Policy This describes the structures and processes by which UEL protects the health and safety of its staff, students and visitors. It confirms that students will receive sufficient information, instruction and induction in relation to health and safety. All students should take reasonable care for their health and safety. They must abide by UEL’s rules and regulations and co-operate with supervisors to enable them to fulfil their obligations. Students must not interfere intentionally, or recklessly misuse anything provided for health and safety. UEL has consulted with its students and staff and has adopted a No Smoking Policy to safeguard the health and well-being of its community. Students are required to comply with this policy which restricts smoking to designated shelters and prohibits the use of electronic cigarettes within any UEL building or near building entrances. For further information on our Healthy Campus initiatives and support please visit the Health and Safety pages . Student Disciplinary Regulations and Procedures (incorporating the student code of conduct) This code is more than a list of things that we should and should not do: it reminds us that we should always consider how our behaviour affects others. The code applies:

• to all students;
• at all sites throughout our estate, and;
• when we represent UEL on business beyond our campus, both in real (face-to-face) and virtual environments.

And outlines expectations of students:

• verbal and physical behaviour should always be polite and respectful;
• behaviour should not impair the engagement, learning or participation of others;
• anti- social behaviour by individuals and groups will not be tolerated.

9) Changes to scheduled programmes

UEL will take all reasonable steps to ensure that the programme of study that you have accepted will conform to the programme specification published on our website and will ensure that the necessary resources required to enable you to meet the required learning outcomes and pass the relevant assessments are available. In order to ensure that our programmes are current and relevant, they are subject to regular review. From time to time, to ensure the maintenance of academic standards and/or compliance with professional body requirements, it may be necessary to amend a module or make adjustments to programme content. Major changes to programmes that in the reasonable opinion of UEL, will have a significant impact on students will involve consultation with students already enrolled on the programme when the changes are proposed. Once any changes are confirmed, UEL will notify all students and applicants of the changes. When UEL reasonably considers that the change may only impact one or more cohorts on the relevant programme, UEL may decide to only consult with the relevant cohort. In the event that we discontinue a programme, we will normally permit existing students to complete the programme within the typical duration of study. In these circumstances, UEL will use reasonable endeavours to continue the programme for existing students without making major changes. If this is not possible, we will support students in changing to another UEL programme on which a place is available, and for which the student is suitably qualified, or assist with transfer to another HEI to complete the programme elsewhere.

10) Changes to these terms

We may change these terms from time to time where, in UEL's opinion, it will assist in the proper delivery of any programme of study or in order to:- (a) Comply with any changes in relevant laws and regulatory requirements; (b) Implement legal advice, national guidance or good practice; (c) Provide for new or improved delivery of any programme of study; (d) Reflect market practice; (e) In our opinion make them clearer or more favourable to you; (f) Rectify any error or mistake; or (g) Incorporate existing arrangements or practice. No variation or amendment to these Terms of Admittance may be made without our prior written agreement. In the event that we agree to transfer you to an alternative programme of study, the transfer will be considered to be a variation to the Terms of Admittance, which shall otherwise remain in full force and existence. If we revise the Terms of Admittance, we will publish the amended Terms of Admittance by such means as we consider reasonably appropriate.;We will use reasonable endeavours to give you notice of any changes before they take effect.

11) Data Protection

UEL is committed to adhering to its obligations under the Data Protection Act 2018 and will act as a Data Controller when it processes your personal data. You can find our registration to the Data controller register on ico.org.uk . UEL processes your personal data fulfil its contractual and legal obligations to students. Personal data that we process about you includes:

• Your contact details and other information submitted during the application and enrolment processes;
• Details of courses, modules, timetables and room bookings, assessment marks and examinations related to your study;
• Financial and personal information collected for the purposes of administering fees and charges, loans, grants, scholarships and hardship funds;
• Photographs, and video recordings for the purpose of recording lectures, student assessment and examinations and for the purposes of university promotion that is in our legitimate interest but still fair to you;
• Information about your engagement with the University such as attendance data and use of electronic services such as Moodle, Civitas and YourTutor;
• Contact details for next of kin to be used in an emergency;
• Details of those with looked after status or those who have left the care system for the provision of support;
• Information related to the prevention and detection of crime and the safety and security of staff and students, including, but not limited to, CCTV recording and data relating to breaches of University regulations;

This is not an exhaustive list, for further information please refer to our fair processing notice pages on uel.ac.uk. In all of its data processing activities, UEL is committed to ensuring that the personal data it collects stores and uses will be processing in line with the data protection principles which can be summarised as:

• Being processed lawfully, fairly and in a transparent manner;
• Collected for specified, explicit and legitimate purposes;
• Adequate, relevant and limited to what is necessary;
• Accurate and, where necessary, kept up to date;
• Kept in a form which permits identification of data subjects for no longer than is necessary;
• Processed in a manner that ensures appropriate security of the personal information;
• Be accountable for, and be able to demonstrate compliance with, the six principles above.

Student Responsibilities You must ensure that:

• All personal data provided to UEL is accurate and up-to-date. You must ensure that changes of address etc. are notified to the Student Hub.
• Students who use UEL's computing facilities may process personal data as part of their studies. If the processing of personal data takes place, students must take responsibility for that processing activity to ensure that it in line with the data protection principles above.
• Students who are undertaking research projects using personal data must ensure that:
• The research subject is informed of the nature of the research and is given a copy of UEL's Fair Processing Notice and this Data Protection Policy.

12) Legal basis for use of data

By agreeing to these Terms of Admittance and enrolling at UEL, you are agreeing to the terms and conditions of a contract for the use of your personal data relating to your enrolment, and if appropriate, registration and ongoing participation on a programme of study. Your personal or special category data will be collected, processed, published and used by UEL, its online learning and teaching services and/or its partners and agents in ways which support the effective management of UEL and your programme of study, to allow for the delivery of bursary schemes and to support improvements to student experience and progression, and are consistent with: The terms of the Data Protection Act 2018; Any notification submitted to the Information Commissioner in accordance with this legislation; and compliance with any other relevant legislation. You have fundamental rights associated with how organisations use your personal data. Further information on data protection and use of your personal data can be found in our Data Protection Policy and on uel.ac.uk.

13) Intellectual property

You are entitled to the intellectual property rights created during your time studying at UEL that would belong to you under the applicable law. There are some programmes where the assignment of certain types of intellectual property to UEL is appropriate. UEL will require the assignment to it of intellectual property rights relating to postgraduate research that is part of an ongoing research programme. Where the nature of the research programme means that some assignment of intellectual property rights to UEL is appropriate, we will take what steps that we can to ensure that your interests are protected. UEL will take reasonable endeavours to ensure:-

• the scope of the assignment is narrow, and is restricted to what is necessary, for example to protect UEL’s legitimate interests in the intellectual property created as party to a research programme;
• the application of the assignment is clearly defined, so that it is clear to you in which circumstances the assignment will apply;
• where the assignment of the intellectual property is appropriate in the circumstances, we will take all reasonable steps to ensure that the rights of the parties are evenly balanced (for example, your work being acknowledged in a publication and, where appropriate, subject to an appropriate revenue sharing scheme)
• where UEL claims ownership of intellectual property rights in relation to a taught programme of study, such treatment of those rights will be made clear in the published information relating to that programme.

14) How we communicate with you

UEL will communicate with you via a variety of channels, including postal letter, e-mail, SMS text message and online notices. To enable this, we request that you provide us with your e-mail address, postal address, and contact telephone number when you first enrol. Throughout your studies, it is important that you keep your contact details up to date. You can view and edit this information by logging into our student portal, UEL Direct at https://uel.ac.uk/Direct . We will create a UEL e-mail account for you after you enrol. Your e-mail address will be your student number, prefixed with a ‘u’ and followed by ‘@uel.ac.uk’ – e.g.: [email protected]. UEL will use this e-mail address to communicate with you and it is important that you regularly check and manage this mailbox for important updates and information. You can access your email account, plus information about our services, news and events by logging into our Intranet, intranet.uel.ac.uk. At the login screen, enter your email address (as above) and password. Your default UEL password will be your date of birth, formulated as DD-MMM-YY, e.g. 31-jan-84. Your UEL email account and associated UEL IT accounts will be deleted not more than 6 months after you graduate or withdraw from your programme of study (if earlier).  

15)University of East London Students' Union

The University of East London Students' Union (UELSU) represents students at UEL. By enrolling at UEL you are automatically granted membership of both UELSU and the National Union of Students (NUS). If you wish to opt out from this membership, please inform UELSU in writing at either [email protected]  or by writing to: Chief Executive, UELSU, University of East London, Docklands Campus, 4-6 University Way, London E16 2RD. UELSU provides a range of services and support to students and can provide advice and representation on any matter affecting the contract between you and UEL. For further information on this support, please visit www.uelunion.org

16) Students studying at partner institutions

If you are undertaking a programme of study at a partner institution you will need to generally abide by the above terms and also those of the partner institution. Further information and support in understanding these terms is available from the Academic Partnership Office -  [email protected] .

17) International students - additional responsibilities

All international students must also comply with UK Visa and Immigration requirements. All international students are required to hold a valid visa which permits study in the UK or hold a Tier 4 visa/have applied for a Tier 4 visa with a Confirmation of Acceptance for Studies issued by UEL. Students who are being sponsored under a Tier 4 student visa must also understand and comply with the responsibilities of their student visa and co-operate with UEL in fulfilling our Tier 4 duties .

18) Equality, Diversity and Inclusion

UEL is committed to working together to build a learning community founded on equality of opportunity – a learning community which celebrates the rich diversity of our student and staff populations and one in which discriminatory behaviour is challenged and not tolerated within our community. Within the spirit of respecting difference, our equality and diversity policies promise fair treatment and equality of opportunity for all regardless of gender, ethnicity, sexual orientation, age, disability or religion/belief (or lack of). In pursuing this aim, we want our community to value and to be at ease with its own diversity and to reflect the needs of the wider community within which we operate. For further information on this inclusive approach to education please visit our Student Policies page .

19) Complaints

We welcome feedback on our programmes and services and facilitate this in a variety of ways, including programme committees, module evaluation forms and surveys. However, if you are dissatisfied with a particular service or programme or the manner in which it has been delivered, you must let the person responsible for that service know as we will always try to resolve matters at the earliest opportunity via informal conciliation. If you are unsure who to approach, please e-mail The Hub who will be able to direct your concerns appropriately. If you remain dissatisfied with a service or programme, or the manner in which it is delivered, you should refer to our formal complaints procedure to have the matter formally addressed. In addition, once you have enrolled onto your programme, you will also have access to the Advice and Information Service offered by UELSU. This access is not available to students studying at partner institutions.

20) Cancellation

If you wish to cancel this contract within 14 days of enrolment onto your programme, you must do so in writing. Any fees that you have paid will be refunded – please see Fees Policy for further information on obtaining a refund.

21) Further guidance

If any of the information in these Terms of Admittance or related policies are unclear or if you have any questions, please contact The Hub for guidance on +44 (0) 208 223 4444 .

22) Right to advice

This is a consumer contract and you are able to obtain independent advice in relation to its terms and conditions from UELSU as well as your local Citizens Advice Bureau.  

23) General

Neither you nor UEL will be liable for failure to perform their obligations under these Terms of Admittance if such failure arises from unforeseeable events, circumstances or causes outside of that party's reasonable control. Examples of such events include, but are not limited to, war, terrorism, industrial disputes, natural disaster, fire and national emergencies. Only you and UEL are parties to these Terms of Admittance. No other person shall have any rights under the Contracts (Rights of Third Parties) Act 1999 to enforce any term of these Terms of Admittance. Failure or delay by you or UEL to exercise any right or remedy provided under this contract shall not constitute a waiver of that or any other right or remedy, nor shall it prevent or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall prevent or restrict the further exercise of that or any other right or remedy. These Terms of Admittance are governed by the law of England and Wales and you and UEL agree to submit to the exclusive jurisdiction of the courts of England and Wales.

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Professional Doctorate Data Science

The first industrial doctorate of its kind will equip you with interdisciplinary research and practical skills for a job in data science or data analytics.

• Award ProfD
• Start date September 2024, January 2025
• Application deadline $value
• Duration Doctorate full-time: 36 months, Doctorate part-time: 72 months
• Mode of study full time, part time
• Delivery on campus

Our Professional Doctorate in Data Science is the first industrial doctorate of its kind, and is supported by The Data Lab innovation centre.

We build on Stirling’s highly successful taught MSc Data Science to equip you with a range of cutting-edge, interdisciplinary research and practical skills and tools, that will lead to an academic or industry job in the area of Data Science, with possible applications to sectors such as life-sciences, finance, engineering, computing, healthcare, fintech or business.

In addition to enhancing students’ employability through work-based learning, the doctorate prepares you to undertake interdisciplinary Data Science research, jointly supervised by world-leading Stirling academics and Data Science industry experts.

The Professional Doctorate consists of a one-year taught programme, based on Stirling MSc programmes in Data Science, and a two-year research programme, to be conducted in collaboration with an industrial partner around industry-relevant research questions. Students could be employees of the industrial partner looking for further training and qualification, or have already established a (potential) collaboration with an industrial partner willing to support the project.

Each of our MSc in Data Science or in Fintech may offer the opportunity to establish a suitable collaboration with an industrial partner, and then grant access to the second year of the Professional Doctorate in Data Science on a research programme agreed with the industrial partner.

Specific projects and collaborations can be considered on a case-by-case basis. An (in principle) agreement with an identified partner company is necessary for the research component of the program.

Top reasons to study with us

Course objectives.

This professional/industrial doctorate is designed to:

• Equip professionals with the required multi-disciplinary skills, and underlying theoretical, practical and transferable knowledge, to undertake practitioner-oriented, impact-led research in data science.
• Give sound training in relevant practical, investigative, analytical and generic skills required for research in the area of data science.
• Experience of data science challenges and applications in a wide range of areas, such as business, healthcare, life science, fintech and scientific disciplines.
• Provide the opportunity to plan, undertake and prepare publication quality research.

Work placements

The research component of the Professional Doctorate in Data Science is a project of industrial interest to be carried out in collaboration with a company supporting the project.

Flexible learning

If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.

Faculty facilities

The Professional Doctorate can be attended both as a full time or part-time course. The taught component is organised around learning material provided online, contact teaching and tutorial hours, and an “open-door” approach allowing students a direct contact with lecturers, providing for great flexibility in the organisation of study. The research component consists of a research project whose development can be planned by agreement between the student, the company and the academic supervisor.

If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.

Entry requirements

Academic requirements.

Students applying may have a variety of backgrounds including:

• numerate and computational degrees (computing, mathematics, physics, engineering)
• medical/clinical, business, marketing or economics background, plus some relevant work (industrial or commercial) experience

Students may also come from other science or engineering backgrounds, to gain applied research and analytical skills that are in high demand in the Scottish job market.

Students with suitable research-oriented Masters degrees in numerate and computational disciplines (computing, mathematics, physics, engineering), will be considered for direct entry to the second year of the Doctoral Training Component, on a case-by-case basis.

An established, in-principle or under-discussion agreement with an industrial partner interested in collaborating and supporting the research component of the programme should be in place.

International entry requirements

View the entry requirements for your country.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:

• IELTS Academic or UKVI 6.0 with a minimum of 5.5 in each sub-skill.
• Pearson Test of English (Academic) 56 overall with a minimum of 51 in each sub-skill.
• IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing.

See our information on English language requirements for more details on the language tests we accept and options to waive these requirements.

Pre-sessional English language courses

If you need to improve your English language skills before you enter this course, our partner INTO University of Stirling offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for entry to this degree.

Find out more about our pre-sessional English language courses .

Course details

You will undertake a number of taught modules to equip you with the skills required for data science research. These modules are taught through lectures, practicals and small group work and are assessed through a variety of course work and exams.

Compulsory modules:

• Mathematical Foundations (10 credits)
• Statistics for Data Science (10 credits)
• Representing and Manipulating Data (20 credits)
• Commercial and Scientific applications (20 credits)
• Relational and non-relational databases (20 credits)
• Data Analytics (20 credits)
• Cluster Computing (20 credits)
• Research Dissertation project (60 credits)

To prepare for the professional doctorate, an independent research project (60 credits) will include a systematic review of an appropriately challenging applied research topic/area, and development of a full Doctorate research proposal as outputs – assessed through an oral viva exam and research poster presentation.

Following the taught component, you will undertake a period of industry-led applied research (360 level 12 credits) by working with experienced academic and industrial supervisors, on original piece(s) of an applied research project. The project could either be a single long project or a portfolio of data-centric projects, depending on the industrial organization’s strategic priority needs. Outcomes will be presented in a doctoral dissertation assessment through a viva examination by internal and external examiners.

The module information below provides an example of the types of course module you may study. The details listed are for the current academic year (September 2023). Modules and start dates are regularly reviewed and may be subject to change in future years.

Course Details

The taught component of the Professional Doctorate spans across the first year and mutates the modules from the various MSc in Data Science, and includes an advanced dissertation project with an assessment of the state of the art and research plan for the next two years.

The research component consists of a period of industry-led applied research, carried out by working with experienced academic and industrial supervisors, on original piece(s) of an applied research project. The project could either be a single long project or a portfolio of data-centric projects, depending on the industrial organisation’s strategic priority needs. Outcomes will be presented in a doctoral dissertation.  

Assessment of the taught component of the program follows the standard assessment of MSc modules and may consists of a variety of assessment strategies, including written assignments, exams,  individual projects, collaborative and group work, lab work, presentations and reports and a dissertation project.

The doctoral dissertation will be assessed through a viva examination by an internal and an external examiner (as in a PhD viva).

Assessment will be tailored to students’ special needs, where appropriate.

Course director

Dr Andrea Bracciali

[email protected] +44 (0)1786 467446

Fees and funding

Fees and costs.

This fee is charged as an annual course fee. If you need to extend your period of study or repeat study, you will be liable for additional fees. Your fees will be held at the same level throughout your course.

For more information on courses invoiced on an annual fee basis, please read our tuition fee policy .

Doctoral loans

If you're domiciled in England or Wales you may be eligible to apply for a doctoral loan from your regional body:

• English students can apply for a loan of up to £28,673 from  Student Finance England .
• Welsh students can apply for a loan of up to £28,395 from  Student Finance Wales .

Additional costs

There are some instances where additional fees may apply. Depending on your chosen course, you may need to pay additional costs, for example for field trips. Learn more about additional fees .

Scholarships and funding

Funding .

Eligible international students could receive a scholarship worth between £4,000-£7,000.  See our range of generous scholarships for international postgraduate students .

University of Stirling alumni will automatically be awarded a fee waiver for the first year of Masters studies through our Stirling Alumni Scholarship .

Applicants from the UK or Republic of Ireland who hold a first-class honours degree or equivalent will automatically be awarded a £2,000 scholarship through our  Postgraduate Merit Scholarship .

If you have the talent, ability and drive to study with us, we want to make sure you make the most of the opportunity – regardless of your financial circumstances.

Learn more about available funding opportunities or use our scholarship finder to explore our range of scholarships.

Cost of living

If you’re domiciled in the UK, you can typically apply to your relevant funding body for help with living costs. This usually takes the form of student loans, grants or bursaries, and the amount awarded depends upon your personal circumstances and household income.

International (including EU) students won’t normally be able to claim living support through SAAS or other UK public funding bodies. You should contact the relevant authority in your country to find out if you’re eligible to receive support.

Find out about the cost of living for students at Stirling

Payment options

We aim to be as flexible as possible, and offer a wide range of payment methods - including the option to pay fees by instalments. Learn more about how to pay

After you graduate

Demand for people with data science skills is projected to grow rapidly in the coming years attracting high salaries.

Our Professional Doctorate in Data Science is run in partnership with industry and is designed to produce graduates with the skills that companies need.

Employability skills

The Doctorate programme, equivalent to an Engineering Doctorate (EngD), is aimed at a clear and distinct market of professionals seeking to enhance their employability opportunities through applied, impact-led research. You’ll learn to develop and validate innovative, data-driven and evidence-based approaches within your chosen career. The programme is geared towards enhancing both your applied, multi-disciplinary research and employability skills in data science.

The doctorate is open to any profession where data-driven and data-intensive research, and its informational derivatives, are central to the development of sustainable business and industry models, including decision-making, project and risk evaluation, policy and technology development. The doctorate research component is relevant to the student’s professional setting and career aspirations.

Companies we work with

Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The Data Lab collaborates with the University of Stirling to help deliver the course, and provide funding and resources for students. You can find out more about the Data Lab from their web site .

We have also developed this professional doctorate in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Data Science projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The first year of the course features a long Industry-led research dissertation project, generally in partnership with a company or technology provider. This provides students with a showcase of their skills to take to employers or launch online.

We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Related courses

• MSc Artificial Intelligence
• MSc Big Data
• MSc Big Data (Online)
• MSc Business Analytics
• MSc Data Science for Business
• MSc Finance and Data Analytics
• MSc Financial Technology (FinTech)
• MSc Marketing Analytics
• MSc Mathematics and Data Science
• MSc Social Statistics and Social Research

Which course would you like to apply for?

Search for another course

DiscoverDataScience.org

PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

• Related Programs

Data Science

Designed to develop core skills in data science, the programme covers a mix of practical and theoretical issues integral to careers in many data driven sectors. Students will learn how to approach real-world data problems, applying their newfound skills in critical thinking, problem solving and analysis.

Key information

• PgCert, PgDip, and MSc options available for self-paced study.
• Start in January or September 2024.
• Learn sought-after coding skills, including Python.

Register your interest Find out more and apply

What you will study

• The programme will teach research methods in data science and help you to understand contemporary issues in the field.
• You will discover methods of datamining, from theory to practical understanding.
• The programme will help you to understand how to create effective information visualisations and how to engage critically with visual displays of data.
• You will employ the full Data Science workflow from data acquisition and processing, through model development and selection, to final deployment and maintenance.
• You will learn optimisation techniques, how to curate and utilise large quantities of data, and how to model and simulate complex systems of data.

How will you be taught?

• You will learn through project and practical work, helping you to understand real-world applications of Data Science.
• Learn through a mix of led and independent study, with synchronous and asynchronous teaching.
• You will have the opportunity to engage in a weekly live panel discussion that will cover data science concepts and specific questions from students.
• Students on the MSc course will undertake a dissertation in data science. Projects based at an employer or sponsor are welcomed (subject to eligibility criteria).

Learning outcomes

Leadership skills and project management.

Cultivate your leadership skills, building your ability to reflect on feedback, to manage projects, and to persevere in the face of challenges.

Problem solving, critical thinking, and analytical skills

Improve your data literacy, whilst learning to think creatively and use your imagination to formulate, design and develop innovative approaches to data analysis.

Knowledge and understanding of programming

Develop specific coding skills in areas such as Excel, Python, Mathematica, Matlab, and mapping.

The Data Science programme will help learners to prepare for, and develop in, a range of careers in data driven sectors and industries. Graduates will leave with sought-after skills across practical and theoretical aspects of data science.

How we can help you to advance your career

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Meet our staff, learn more, and ask questions about how our courses can work for you.

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Applied Data Science

For students interested in research, the MSc degree provides the opportunity to start a PhD research project in any of the related topics at a university in the Netherlands or abroad. Such positions are regularly available within Utrecht University. There are ample contacts with other universities as well, partly through the nationwide Dutch research school SIKS (School for Knowledge and Information Systems). Some positions are sponsored by companies or the Dutch research council. Graduates may also find challenging positions in R&D departments of international companies, governments, and the banking and insurance sectors.

Doing a PhD

If you have completed your Master's programme, and you are enthusiastic about doing research in your field, then maybe doing a Doctorate (PhD) will interest you. A Doctor’s degree is the highest academic degree awarded by a Dutch university. You start as 'assistant in training' (aio) or 'researcher in training' (oio).

At Utrecht University

At Utrecht University you take part in education in one of the Graduate Schools and often also teach students. During the four-year PhD programme you work under the guidance of a professor on creating a research project that results in a dissertation or a series of articles in scientific journals. You can search for positions on research projects on offer or a position whereby you are free to submit your own research proposal. Read more on doing a  PhD  at Utrecht University. 

Other options

The best way to find a PhD position is through networking with the professor in the field you wish to specialize. Another option is to search via  www.academictransfer.nl . Here you can also find more information on doing PhD research in the Netherlands. 

Follow Utrecht University

Utrecht University Heidelberglaan 8 3584 CS Utrecht The Netherlands Tel. +31 (0)30 253 35 50

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LSE PhD Studentship in Data Science

For 2023 entry, LSE is offering a doctoral studentship for PhD study affiliated to the Data Science Institute (DSI). 

Applications are welcome from both students applying to core data science programmes (Statistics, Mathematics, or Methodology) as well as from applied departments across the School, as long as their projects involve data science or computational social science methods.

The successful student will join a growing cohort of existing DSI-hosted PhD students as well as a regular stream of visiting PhD students in data science. 

Eligibility

Selection for this studentship is on the basis of outstanding academic merit and research potential. This relates both to your past academic record and to an assessment of your likely aptitude to complete a PhD in your chosen topic in the time allocated.

Scholarship amount

The LSE Data Science PhD Studentship is tenable for four years and covers full fees along with an annual stipend of £19,668 (2022/23 rate).

How to apply

To be considered, you must submit a complete application (including references, proposal, marked work etc) by the funding deadline below.  

• funding deadline for all LSE PhD Studentships for 2023 entry: 13 January 2023

For more information visit  how to apply  for a place on a PhD programme.

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Undergraduate fees and funding Details on available scholarships, bursaries, loans and tuition fees

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Phd 2023/24 in DATA SCIENCE

What is funded.

4 PhD sholarships

Eligibility

See  https://phd.uniroma2.it/web/DATA-SCIENCE_nD1028.aspx

Organisation

Attachments.

The responsibility for the funding offers published on this website, including the funding description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.

• CHE University Ranking
• DAAD database on admission requirements
• Help and Advice

International Programmes 2023/2024

Data Science Data Science

University of potsdam • potsdam.

• Course details
• Online learning
• Costs / Funding
• Requirements / Registration

The programme is taught in English.

The interdisciplinary field of data science deals with methods for using data to automatically produce knowledge, insights, and models for prognosis, risk, and action. The Master's degree programme, which is taught in English, connects machine learning, statistical data analysis, natural scientific methods of data assimilation, and business analytics. The programme offers broad and interdisciplinary structured training in methods and is characterised by a strong emphasis on practice and research.

The interdisciplinary structured Master's degree in data science combines content from computer science, mathematics, information systems, and the natural sciences. Core courses provide you with an overarching understanding of machine learning and deep learning, statistical data analysis, data assimilation, business analytics, and big data infrastructures. More specialised courses help you engage with the current state of research in the chosen areas of focus.

In seminars, you will work through complex topics, and in the module of applied data science, you will apply the competences you have acquired in a practical manner. In the research module, you will be connected to a research project at the University of Potsdam or one of Potsdam's many research institutions. An internship in industry is also an option. Berlin/Potsdam's lively start-up scene and many big data companies offer ample opportunities for internships.

In the four-semester Master's programme, you will earn a total of 120 credit points, consisting of the modules and your Master's thesis. For additional information, please consult the subject-specific degree regulations  or the  Departmental Advisory Office .

An internship in industry is a possible option. Berlin/Potsdam's lively start-up scene and many big data companies offer ample opportunities for internships.

• Online study material provided by institution

No tuition is charged for studying at the University of Potsdam (with the exception of a few continuing education courses). There is only an administrative fee, which currently amounts to 316 EUR. This semester contribution includes a semester ticket entitling you to free use of public transport all over Brandenburg and Berlin.

Living in Germany is rather cheap compared to other countries in Northern and Western Europe. Monthly living expenditures of students at the University of Potsdam vary between 870 and 1,200 EUR, largely depending on accommodation. A more detailed list of the average monthly expenditures of our students can be found here .

Applying for a Master's degree generally requires you to hold an undergraduate degree, such as a Bachelor's degree. A first degree in either computer science or mathematics qualifies you for this Master's degree in any case. A degree in information systems or natural sciences qualifies you if your first degree strongly emphasised content from the areas of computer science or mathematics. Depending on your background, bridge modules can complete gaps in the other respective discipline.

You can find the exact prerequisites for admission in the subject-specific admission regulations  for the Master's degree programme in data science.

The programme requires proof of good English skills corresponding at least to level C1 of the Common European Framework of Reference for Languages.

Please submit your application online and via mail to uni-assist:

Universität Potsdam c/o uni-assist e.V. 11507 Berlin Germany

Job opportunities for students alongside their studies are numerous. Depending on personal and professional skills, students can find jobs in the service sector (e.g. in restaurants, supermarkets, cinemas, museums, theatres, etc.) or work as student assistants at the University of Potsdam or at one of the many non-university research institutions located in the area. The German capital, Berlin, is located just around the corner, and students can also find jobs there. Please see this website for further information.

There are several student dormitories in Potsdam. These are administered exclusively by Potsdam's Association for Student Affairs ("Studentenwerk Potsdam"). You can apply for a room in one of these dormitories if you are under the age of 30. If you are a Master's student, you can only apply if the degree at the University of Potsdam is your first Master's programme. Additionally, there are also a few private dormitories in Potsdam, to which these conditions do not apply. However, many students prefer to find an apartment in Potsdam or Berlin, which they often share with fellow students. The housing market is tight, so please start looking for an accommodation as early as possible.

For information on how to register for a dorm room or find accommodation elsewhere, please click this link .

The Career Service of the University of Potsdam supports students and graduates who want to actively shape their careers. The aim is to ensure comprehensive career orientation and to provide opportunities for the development of professional skills. The Career Service offers workshops, seminars, and individual counselling. There are information pages on a wide range of occupational fields as well as a database of internships and jobs. Many of the services are also available in English.

• Specialist counselling
• Visa matters
• Welcome event
• Buddy programme

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Language Technologies Institute

School of computer science.

Master of Computational Data Science

The Master of Computational Data Science (MCDS) program focuses on engineering and deploying large-scale information systems, and includes concentrations in Systems, Analytics, and Human-Centered Data Science.

Requirements

The MCDS program offers three majors: Systems, Analytics, and Human-Centered Data Science. All three require the same total number of course credits, split among required core courses, electives, data science seminar and capstone courses specifically defined for each major. The degree can also be earned in two different ways, depending on the length of time you spend working on it. Regardless of the timing option, all MCDS students must complete a minimum of 144 units to graduate.

Here are the options:

• Standard Timing — a 16-month degree consisting of study for fall and spring semesters, a summer internship, and fall semester of study. Each semester comprises a minimum of 48 units. This timing is typical for most students. Students graduate in December.
• Extended Timing — a 20-month degree consisting of study for fall and spring semesters, a summer internship, and a second year of fall and spring study. Each semester comprises a minimum of 36 units. Students graduate in May.

Core Curriculum

All MCDS students must complete 144 units of graduate study which satisfy the following curriculum:

• Five (5) MCDS Core Courses (63 units)
• Three courses (3) from one area of concentration curriculum (36 units)
• Three (3) MCDS Capstone courses (11-635, 11-634 and 11-632) (36 units)
• One (1) Electives: any graduate level course 600 and above in the School of Computer Science (12 units)

Area of Concentration

• During the first two semesters in the program, all students take a set of five (5) required core courses: 11-637 Fundamentals of Computational Data Science, 15-619 Cloud Computing, 10-601 Machine Learning, 05-839 Interactive Data Science, and 11-631 Data Science Seminar.
• By the end of the first semester, all students must select at least one area of concentration — Systems, Analytics, or Human-Centered Data Science — which governs the courses taken after the first semester.
• To maximize your chances of success in the program, you should consider which concentration area(s) you are best prepared for, based on your educational background, work experience, and  areas of interest as described in your Statement of Purpose.
• You are strongly encouraged to review the detailed curriculum requirements for each concentration area, in order to determine the best fit given your preparation and background.

For a complete overview of the MCDS requirements read the  MCDS Handbook .

To earn an MCDS degree, students must pass courses in the core curriculum, the MCDS seminar, a concentration area, and electives. Students must also complete a capstone project in which they work on a research project at CMU or on an industry-sponsored project.

In total, students must complete 144 eligible units of study, including eight 12-unit courses, two 12-unit seminar courses, and one 24-unit capstone course. Students must choose at minimum five core courses. The remainder of the 12-unit courses with course numbers 600 or greater can be electives chosen from the SCS course catalog. Any additional non-prerequisite units taken beyond the 144 units are also considered electives.

Students who plan to select the Systems concentration may wish to enroll in 15-513 “Introduction to Computing Systems” during the summer session preceding their enrollment in the program; this course is a prerequisite for many advanced Systems courses, so it should be completed during Summer if you wish to enroll in advanced Systems courses in the Fall.

Click here   to see the MCDS Course Map.

Some example courses of study are included below.

Example 1: Analytics Major, 16 Months

Example 2: Systems Major, 16 Months

Example 3: Human-Centered Data Science Major, 16 Months

Carnegie Mellon's School of Computer Science has a centralized  online application process . Applications and all supporting documentation for fall admission to any of the LTI's graduate programs must be received by the application deadline. Incomplete applications will not be considered.  The application period for Fall 2024 is now closed. Information about the Fall 2025 admissions cycle will be available in summer 2024.

Application Deadlines

Fee Waivers

Fee waivers may be available in cases of financial hardship, or for participants in select "pipeline" programs. For more information, please refer to the  School of Computer Science Fee Waiver page .

The School of Computer Science requires the following for all applications:

• A GPA of 3.0 or higher.
• GRE scores: These must be less than five years old. Our Institution Code is 2074; Department Code is 0402. (This requirement is waived for CMU undergrads.)
• The GRE At Home test is accepted but we prefer you take the GRE at a test center if possible.
• Unofficial transcripts from each university you have attended, regardless of whether you received your degree there.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation
• A short (1-3 minutes) video of yourself. Tell us about you and why you are interested in the MCDS program. This is not a required part of the application process, but it is STRONGLY suggested.  
• Proof of English Language Proficiency

Proof of English Language Proficiency: If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo. We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored.

We do not issue waivers for non-native speakers of English. In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university. We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States. No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Applicants applying to MCDS are required to submit scores from an English proficiency exam taken within the last two years. Scores taken before Sept. 1, 2021, will not be accepted regardless of whether you have previously studied in the U.S. For more information about their English proficiency score policies, visit the  MCDS  admission website.  Successful applicants will have a minimum TOEFL score of 100, IELTS score of 7.5, or DuoLingo score of 120. Our Institution Code is 4256; the Department Code is 78. Additional details about English proficiency requirements are provided on the  FAQ  page. 

Applications which do not meet  all  of these requirements by the application deadline (see above) will not be reviewed.

For more details on these requirements, please see the  SCS Master's Admissions page.

In addition to the SCS guidelines, the LTI requires:

• Any outside funding you are receiving must be accompanied by an official award letter.

No incomplete applications will be eligible for consideration.

For specific application/admissions questions, please contact  Jennifer Lucas  or Caitlin Korpus .

Program Contact

For more information about the MCDS program, contact Jennifer Lucas or Caitlin Korpus

Jennifer Lucas

Caitlin korpus, online graduate certificate program, program handbook.

Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

At a Glance

Start Date May 2024

Application Deadlines Rolling Admissions

We are still accepting applications for a limited number of remaining spots to start in Summer 2024. Apply today to secure your space in the program.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.

Rawls College of Business

Master of science in data science.

Ranked the No. 3 best online, non-MBA program in the nation in 2022 , the Rawls College Master's in Data Science (MSDS) program provides graduates with the technical expertise needed to lead in the digital frontier. Through our 36-hour, STEM-designated program, learn how to manage, analyze and understand complex data to make strategic decisions. Upon graduation, you will have the skills and knowledge needed to be an agile data scientist capable of making impactful decisions across a variety of business settings and industries.

program highlights

Flexible format, on-campus or online classes.

Options to complete coursework on campus or online allow you to choose the course modality that best fits your personal needs.

One or Two-Year Options

Complete your degree in as little as a year, or take fewer classes per semester by selecting the online, two-year option.

Optional Practical Training (OPT) Eligible

International students may qualify to work in the U.S. for up to three years after receiving their degrees.

Cutting-Edge Curriculum

We prioritize the real-world application of knowledge and skills to best support students who want to accelerate their careers in data science, business analytics, business intelligence and big data fields. Through our comprehensive curriculum, you will learn how to use advanced technologies and statistical methods to manipulate data and translate findings into actionable organizational strategies.

Classes include foundational building blocks for today's data scientists:

Statistics for Data Science

Scripting Languages

Database Concepts

Data Technology Environments

Big Data Strategy

Business Intelligence

Multivariate Analysis

Time Series Analysis

Simulation & Optimization

Machine Learning

Decision Theory and Business Analytics

Big Data Security

program format

The MSDS program requires 36-credit hours, consisting of specialized data science courses. This is a lock-step program, requiring students to take classes in a specific order, as concepts build on each other. The program begins in the summer, and summer courses are four to five weeks in length. Fall and spring courses are seven to eight weeks in length. Coursework for the one-year program can be completed on campus or online. The two-year program is available online only.

Working professionals experience their core courses together, creating a stimulating cohort-based learning environment. During your tenure in the program, you will build relationships with peers, emanating from diverse backgrounds and industries, resulting in a larger professional network upon graduation.

View sample degree programs »

Our MSDS faculty members include engaged technology practitioners who utilize real-world experience to inspire their instruction. Their areas of expertise include computer-aided decision making, information requirements determination, operations management, health care analytics, information economics and more.

Meet our faculty »

class profile

At Rawls College, we believe diversity drives opportunities for collaboration and learning. Together, we benefit from the many perspectives, skills and experiences our students from all over the world bring to our learning environment.

supporting your success

Rawls Career Management Center

Whether you are looking to switch careers or advance on your current path, the Rawls Career Management Center (CMC) is dedicated to supporting your success. The staff in the CMC can help you explore professions and industries, learn strategic career advancement techniques, and connect you with top employers.

Techsan Connection

The Techsan Connection is a free, online platform for Texas Tech alumni. Through the platform, alumni can apply to jobs, reconnect with fellow classmates, network with industry professionals and volunteer to mentor current students.

The admission process is the first step toward earning your degree. We will work closely with you to ensure your application process is personal, simple and successful.

Application Requirements

While no prior work experience is required, applicants must have a bachelor's degree. Most applicants have an education or work background in computer science, management information systems, science, engineering, or similar fields. Basic knowledge of computer programming software such as R, SQL, and Python will be beneficial throughout the program's coursework. Additionally, applicants will benefit from prior completion of coursework in calculus, statistics and probability.

Unofficial Transcripts

Applicants must submit unofficial transcripts from any degree-awarding college or university, as well as any post-secondary institution attended. 

Applicants must submit a detailed current resume, indicating professional work experience—including start and end dates (month and year) for each position held. Provide accomplishments and skills acquired, including managerial experience.

GMAT Scores

The summer 2023 intake will not require a GRE or GMAT for application, but submission of scores are encouraged. 

We don't have a set minimum or maximum requirement for test scores. We review students holistically taking the application in its entirety into consideration.

English Proficiency for International Students

All international applicants must provide proof of English proficiency before their applications can be considered for admission. Only your most recent measure of English proficiency is considered for admission purposes. This test is waived only for graduates of U.S. universities or universities in English proficiency-exempt countries. Applicants who have completed at least two consecutive years at a college or university in the U.S. or in an English proficiency-exempt country are also exempt from the English proficiency requirement.

Application Deadlines

Summer Entry: May 1

International students are encouraged to apply at least six months in advance when possible.

student resources

• Prospective Students
• Current Students

Program Questions

[email protected] 806.742.3184

Cy Cawthron 806.834.1069

Events@Rawls

Professional mba weekend classes.

Saturday, June 1, 2024 - Sun , June 2, 2024 (all day)

Where: Rawls College of Business

Saturday, June 29, 2024 - Sun , June 30, 2024 (all day)

Where: Center for Business Communications Room 139

Contact TTU

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U.S. News Releases 2024 Best Graduate Programs Rankings

Find the top-ranked graduate schools in business, education, law, nursing and other fields.

U.S. News Ranks Best Graduate Schools

Photo Library

To help prospective graduate students find a school that fits their needs, U.S. News released the 2024 rankings for multiple graduate fields.

Depending on the job or field, earning a graduate degree may lead to higher earnings, career advancement and specialized skill development.

But with several types of degrees and hundreds of graduate schools, it can be difficult to narrow down the options. To help prospective graduate students find a school that fits their needs, U.S. News released its 2024 Best Graduate Schools rankings today. They evaluate business, education, fine arts, health, law, library studies, nursing, public affairs, science, and social sciences and humanities graduate programs. Medical school and engineering rankings are not being released at this time.

A notable methodology change includes a new salary indicator based on profession in the business rankings.

Additionally, for the first time in four years, there are new rankings for a blend of doctoral and master's programs in audiology, occupational therapy, physical therapy, pharmacy, nurse midwifery and speech-language pathology. Graduate programs in nurse anesthesia and social work are also ranked for the first time since 2016 and 2022, respectively. Those and other specialty rankings are based on reputation ratings from scholars at other surveyed schools.

Read each program's specific methodology for the most detailed explanations of all the changes. The rankings are one source of information among many that prospective college students can use to inform their college decision. Below is a summary of the top-ranked schools in four major graduate program areas:

Best Law Schools

Best business schools, best nursing schools, best education schools.

Among the top 10 law schools . Yale Law School in Connecticut and California-based  Stanford Law School shared the top spot again. The  University of Chicago Law School in Illinois maintained its No. 3 rank, followed by a four-way tie at No. 4: Duke University School of Law in North Carolina, Harvard Law School in Massachusetts, the University of Pennsylvania Carey Law School and the University of Virginia School of Law .

Columbia Law School in New York ranked No. 8 again, while there was a three-way tie for No. 9: New York University School of Law , Northwestern University's Pritzker School of Law in Illinois and the University of Michigan—Ann Arbor Law School .

Looking beyond the top 10, multiple law schools moved up in the rankings. William & Mary Law School in Virginia, for instance, jumped nine spots from a tie at No. 45 to a five-way tie at No. 36.

U.S. News also ranked 13 law specialties: business/corporate, clinical training, constitutional, contracts/commercial, criminal, dispute resolution, environmental, health care, intellectual property, international, legal writing, tax and trial advocacy. (You can filter by specialty on the  main ranking page .)

Meanwhile, in the  part-time law school rankings – which consists of law schools with at least 20 part-time students enrolled in fall 2022 and fall 2023 – the top three stayed the same. The  Georgetown University Law Center in Washington, D.C., is once again at the top while D.C.-based  George Washington University Law School , now No. 3, switched places with the  Fordham University School of Law in New York City, which claimed second place.

Previously ranked at No. 3 and No. 6 respectively, the University of Pennsylvania's Wharton School and Stanford Graduate School of Business took the top spot in this year's full-time MBA program rankings . Northwestern's Kellogg School of Management and the University of Chicago's Booth School of Business moved down from their previous places in the top two to tie at No. 3.

While the top 10 mostly consists of the same schools as last year, both the Haas School of Business at the University of California, Berkeley and the University of Virginia's Darden School of Business joined those ranks this year. UC Berkeley rose from a three-way tie at No. 11 to a three-way tie at No. 7, while UVA moved up four spots from No. 14 to a tie at No. 10.

Farther down the full-time MBA rankings, there were some big changes. For example, Pitt's Joseph M. Katz Graduate School of Business soared 39 spots from a tie at No. 86 to a tie at No. 47.

Meanwhile, the very top of the part-time MBA rankings looks similar to last year, with the same schools in the top 5: UChicago, UC Berkeley, Northwestern, NYU's Leonard N. Stern School of Business and the Anderson School of Management at the University of California—Los Angeles. But UChicago took the No. 1 spot from UC Berkeley this year.

Moving up from No. 2, Johns Hopkins University School of Nursing in Maryland tied with Emory University's Nell Hodgson Woodruff School of Nursing in Georgia to claim the top spot in this year's nursing master's program rankings. Duke University School of Nursing in North Carolina climbed up by one to claim the third spot.

Johns Hopkins ranked No. 1, as it did last year, in the Doctor of Nursing Practice program rankings. George Mason University School of Nursing in Virginia – which reported more graduates and resources per faculty – soared from a four-way tie at No. 39 to take the No. 2 spot. Duke tied with the University of Washington School of Nursing to round out the top three.

Duke also ranked No. 1 in all of the ranked nursing master's nursing practice specialties, including administration, family, both acute and primary care adult gerontology, and mental health.

Once again, Teachers College, Columbia University in New York was No. 1 in the graduate education schools rankings. This year, however, it tied with the University of Wisconsin—Madison School of Education , which climbed two spots.

The University of Michigan—Ann Arbor's School of Education dropped from the top position to tie with the UCLA School of Education and Information Studies at No. 3. UCLA was previously tied at No. 7.

U.S. News also ranks nine education specialties, with the College of Education at Michigan State University claiming the top spot in the following categories: curriculum and instruction, educational administration, elementary teacher education, higher education administration and secondary teacher education.

Searching for a grad school of education? Access our  complete rankings  of Best Graduate Schools.

Grad Degree Jobs With $100K+ Salaries

Tags: students , graduate schools , medical school , business school , law school , education graduate school , engineering graduate school , MBAs , nursing programs

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