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10 Compelling Machine Learning Ph.D. Dissertations for 2020

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 19, 2020 Daniel Gutierrez, ODSC

As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and reveal fertile areas of research. Other sources of valuable research developments are in the form of Ph.D. dissertations, the culmination of a doctoral candidate’s work to confer his/her degree. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Their dissertations are highly focused on a specific problem. If you can find a dissertation that aligns with your areas of interest, consuming the research is an excellent way to do a deep dive into the technology. After reviewing hundreds of recent theses from universities all over the country, I present 10 machine learning dissertations that I found compelling in terms of my own areas of interest.

[Related article: Introduction to Bayesian Deep Learning ]

I hope you’ll find several that match your own fields of inquiry. Each thesis may take a while to consume but will result in hours of satisfying summer reading. Enjoy!

1. Bayesian Modeling and Variable Selection for Complex Data

As we routinely encounter high-throughput data sets in complex biological and environmental research, developing novel models and methods for variable selection has received widespread attention. This dissertation addresses a few key challenges in Bayesian modeling and variable selection for high-dimensional data with complex spatial structures. 

2. Topics in Statistical Learning with a Focus on Large Scale Data

Big data vary in shape and call for different approaches. One type of big data is the tall data, i.e., a very large number of samples but not too many features. This dissertation describes a general communication-efficient algorithm for distributed statistical learning on this type of big data. The algorithm distributes the samples uniformly to multiple machines, and uses a common reference data to improve the performance of local estimates. The algorithm enables potentially much faster analysis, at a small cost to statistical performance.

Another type of big data is the wide data, i.e., too many features but a limited number of samples. It is also called high-dimensional data, to which many classical statistical methods are not applicable. 

This dissertation discusses a method of dimensionality reduction for high-dimensional classification. The method partitions features into independent communities and splits the original classification problem into separate smaller ones. It enables parallel computing and produces more interpretable results.

3. Sets as Measures: Optimization and Machine Learning

The purpose of this machine learning dissertation is to address the following simple question:

How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?

Optimization and machine learning have proved remarkably successful in applications requiring the choice of single vectors. Some tasks, in particular many inverse problems, call for the design, or estimation, of sets of objects. When the size of these sets is a priori unknown, directly applying optimization or machine learning techniques designed for single vectors appears difficult. The work in this dissertation shows that a very old idea for transforming sets into elements of a vector space (namely, a space of measures), a common trick in theoretical analysis, generates effective practical algorithms.

4. A Geometric Perspective on Some Topics in Statistical Learning

Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this dissertation is to develop a fundamental understanding of statistical procedures for estimation and hypothesis testing from a non-asymptotic point of view, where both the sample size and problem dimension grow hand in hand. A range of different problems are explored in this thesis, including work on the geometry of hypothesis testing, adaptivity to local structure in estimation, effective methods for shape-constrained problems, and early stopping with boosting algorithms. The treatment of these different problems shares the common theme of emphasizing the underlying geometric structure.

5. Essays on Random Forest Ensembles

A random forest is a popular machine learning ensemble method that has proven successful in solving a wide range of classification problems. While other successful classifiers, such as boosting algorithms or neural networks, admit natural interpretations as maximum likelihood, a suitable statistical interpretation is much more elusive for a random forest. The first part of this dissertation demonstrates that a random forest is a fruitful framework in which to study AdaBoost and deep neural networks. The work explores the concept and utility of interpolation, the ability of a classifier to perfectly fit its training data. The second part of this dissertation places a random forest on more sound statistical footing by framing it as kernel regression with the proximity kernel. The work then analyzes the parameters that control the bandwidth of this kernel and discuss useful generalizations.

6. Marginally Interpretable Generalized Linear Mixed Models

A popular approach for relating correlated measurements of a non-Gaussian response variable to a set of predictors is to introduce latent random variables and fit a generalized linear mixed model. The conventional strategy for specifying such a model leads to parameter estimates that must be interpreted conditional on the latent variables. In many cases, interest lies not in these conditional parameters, but rather in marginal parameters that summarize the average effect of the predictors across the entire population. Due to the structure of the generalized linear mixed model, the average effect across all individuals in a population is generally not the same as the effect for an average individual. Further complicating matters, obtaining marginal summaries from a generalized linear mixed model often requires evaluation of an analytically intractable integral or use of an approximation. Another popular approach in this setting is to fit a marginal model using generalized estimating equations. This strategy is effective for estimating marginal parameters, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. Thus, there exists a need for a better approach.

This dissertation defines a class of marginally interpretable generalized linear mixed models that leads to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model. The distinguishing feature of these models is an additive adjustment that accounts for the curvature of the link function and thereby preserves a specific form for the marginal mean after integrating out the latent random variables. 

7. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media

The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups in online social media. Beginning with a unique typology for detecting abusive language, the work outlines the distinctions and similarities of different abusive language subtasks (offensive language, hate speech, cyberbullying and trolling) and how we might benefit from the progress made in each area. Specifically, the work suggests that each subtask can be categorized based on whether or not the abusive language being studied 1) is directed at a specific individual, or targets a generalized “Other” and 2) the extent to which the language is explicit versus implicit. The work then uses knowledge gained from this typology to tackle the “problem of offensive language” in hate speech detection. 

8. Lasso Guarantees for Dependent Data

Serially correlated high dimensional data are prevalent in the big data era. In order to predict and learn the complex relationship among the multiple time series, high dimensional modeling has gained importance in various fields such as control theory, statistics, economics, finance, genetics and neuroscience. This dissertation studies a number of high dimensional statistical problems involving different classes of mixing processes. 

9. Random forest robustness, variable importance, and tree aggregation

Random forest methodology is a nonparametric, machine learning approach capable of strong performance in regression and classification problems involving complex data sets. In addition to making predictions, random forests can be used to assess the relative importance of feature variables. This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness. 

10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery

This dissertation solves two important problems in the modern analysis of big climate data. The first is the efficient visualization and fast delivery of big climate data, and the second is a computationally extensive principal component analysis (PCA) using spherical harmonics on the Earth’s surface. The second problem creates a way to supply the data for the technology developed in the first. These two problems are computationally difficult, such as the representation of higher order spherical harmonics Y400, which is critical for upscaling weather data to almost infinitely fine spatial resolution.

I hope you enjoyed learning about these compelling machine learning dissertations.

Editor’s note: Interested in more data science research? Check out the Research Frontiers track at ODSC Europe this September 17-19 or the ODSC West Research Frontiers track this October 27-30.

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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PhD Thesis on "Big Data in Official Statistics"

The explosion in the amount of data, called “data deluge”, is forcing to redefine many scientific and technological fields, with the affirmation in any environment of Big Data as a potential source of data. Official statistics institutions a few years ago started to open up to external data sources such as administrative data. The advent of Big Data is introducing important innovations: the availability of additional external data sources, dimensions previously unknown and questionable consistency, poses new challenges to the institutions of official statistics, imposing a general rethinking that involves tools, software, methodologies and organizations. The relative newness of the field of study on Big Data requires first of all an introduction phase, for addressing the problems of definition and for defining the areas of technology involved and the possible fields of application. The challenges that the use of Big Data poses to institutions that deal with official statistics are then presented in detail, after a brief discussion of the relationship between the new "data science" and statistics. Although at an early stage, there is already a number, limited but growing practical experience in the use of Big Data as a data source for use in statistics by public (and private) institutions. The review of these experiences can serve as a stimulus to address in a more conscious and organized way the challenges that the use of this data source requires all producers of official statistics. The worldwide spread of data sources (web, e-commerce, sensors) has also prompted the statistical community to take joint action to tackle the complex set of methodological, technical and legal problems. And so many national statistical institutes along with the most prestigious international organizations have initiated joint projects that will develop in the coming years to address the complex issues raised by Big Data for statistical methodology and computer technology.

Related Papers

Intersections

Johanna Giczi

More than five years ago Eurostat started a project with the aim to 'tame' sources of Big Data in a way that they can be incoporated into official statistical systems. In order to solve the problems a statistician might be faced with during the official statistical application of Big Data, first of all, we give an overview of traditional data collection, and then point to the differences one has to face when dealing with Big Data. We introduce common sources of data (traditional, administrative) and highlight the ways huge sets of data are different compared to them. Next, we discuss characteristics of Big Data versus traditional statistical methods based on the qualitative criteria of official statistics, and we also elaborate on the problems of analysing Big Data. Finally, we provide a list of use cases for Big Data in official statistical data collections.

Carlo Vaccari

Gregory Farmakis

The aim of this paper is to assess the feasibility of employing novel methodologies for producing high quality Official Statistics based on Big Data. Big Data can be described as high volume, high velocity and high variety of information that require new methods of processing. Big Data is such a rich source of data that the Official Statistics community cannot ignore. Official statistics are statistics published by government agencies or other public bodies that aim to response to policy needs. They are based almost exclusively on survey data collections and administrative data. On the other hand, Big Data challenge the way we think about data assets, the sources we collect them from and the way we analyze them. This illustrates that paradigms are shifting, since a reverse approach of designing statistics is being applied. With Big Data, it is fundamental to explore the vast amounts of data available first and then to decide on the quantities to be measured. Thus, inference techniques used for Official Statistics will need to make a shift too. In this paper, we explore the potential of using Big Data sources as input for supplemeting or replacing Official Statistics is examined. The work is carried out under the framework of a European Commission / Eurostat project. Five sources are examined: (a) the Automatic Identification System (AIS) records for replacing Official Maritime Transport Statistics on vessel movement, (b) a classifieds site concerning house sales and rental prices in order to produce Housing Price Official Statistics, (c) the Facebook and Twitter content to produce well-being quality of life measures, complementary to Official Statistics, (d) Credit card transaction data for supplementing consumption expenditure statistics and (e) the ‘‘δι@ύγεια’’ site publishing government expenditure decisions for national accounting purposes. The potential of using the selected Big Data sources as input of Official Statistics is investigated in terms of the quality of the statistics they produce. The quality of the data is disscussed according to the dimensions defined by the European Statistical System, namely: a) relevance, b) accuracy, c) timeliness and puctuality, d) accessibility and clarity and e) coherence. The cost involved in the production prossess is also discussed. The evaluation of the effect of methodological restrictions on the overall quality of the statistics produced reveals several potential benefits as well as disadvantages. Among all, Big Data are characterized by the amount of information and the frequency at which they are produced. The high geographical coverage and the accuracy of the data, are some of the pros. On the other hand, a major issue concerning Big Data is the accessibility due to confidentiality or other policies. In addition, the volume of the data increases further the processing needs for validation. It is highlighted, that the quantities produced can be used supplementary to the respective Official Statistics’ indicators, while at most of the cases, it is not feasible to replace them. However, exploiting the vast ammounts of data available in a methodologically sound way may enhance the fast production of low cost and high quality official statistics. That is, Big Data is a field that requires new tailored methods to accelerate the analysis of large amount of data.

Big Data is an extremely interesting data source for statistics. Since more and more data is generated in our modern world and is digitally stored, it could certainly be used to replace traditional sources or provide additional information for official statistics. Especially given declines in survey response rates, information gathered from Big Data is an interesting addition. However, extracting statistically-relevant information from Big Data sources is not an easy task. In this paper the current state of the art of research on the use of Big Data for official statistics at Statistics Netherlands is described. The paper is based on the real world experiences of the authors obtained during these studies. The topics discussed are related to Big Data methodology, privacy and security concerns and the skills required for successfully employing Big Data. Introduction Big Data is a term that one hears more and more often at conferences, meetings and seminars. Since its first introductio...

Rob Kitchin

The development of big data is set to be a significant disruptive innovation in the production of official statistics offering a range of opportunities, challenges and risks to the work of National Statistical Institutions (NSIs). This paper provides a synoptic overview of these issues in detail, mapping out the various pros and cons of big data for producing official statistics, examining the work to date by NSIs in formulating a strategic and operational response to big data, and plotting some suggestions with respect to ongoing change management needed to address the use of big data for official statistics.

Journal of Official Statistics

More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. This article discusses the exploration of both opportunities and challenges for official statistics associated with the application of Big Data. Experiences gained with analyses of large amounts of Dutch traffic loop detection records and Dutch social media messages are described to illustrate the topics characteristic of the statistical analysis and use of Big Data.

More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because of the fact that these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. However, first experiences obtained with analyses of large amounts of Dutch traffic loop detection records, call detail records of mobile phones and Dutch social media messages reveal that a number of challenges need to be addressed to enable the application of these data sources for official statistics. These and the lessons learned during these initial studies will be addressed and illustrated by examples. More specifically, the following topics are discussed: the three general types of Big Data discerned, the...

Giulio Barcaroli

1. Big data are becoming more and more important as additional data sources for Official Statistics (OS). Istat set up three projects aimed to experiment three different roles of Big data sources, namely: (i) “new” sources enabling access to data not yet collected by Official Statistics (ii) “additional” sources to be used in conjunction with traditional data sources and (iii) “alternative” sources fully or partially replacing traditional ones.

Serena Signorelli

This paper tries to give a short overview on the use of Big Data for statistical purposes, on the problems that arise with this kind of data, especially about quality, shows some applications that havee been done using Big Data in combination with traditional survey data. Finally, a small-scale case study is presented by critically highlighting problems and solutions arising the transition form Big Data to information.

It is widely recognised that important methodological and quality issues are associated with Big Data. Especially selectivity issues become prominent when trying to apply established statistical methods. Using sampling theory as the framework for the production of estimates based on Big Data may not be effective, especially if the data cannot be linked to a known population of units. The question arises to what extent an approach originally developed for survey based statistics can be applied to Big Data. The paper discusses possible quality approaches to the production of official statistics when dealing with Big Data, for instance the compilation of statistics not aimed at predefined populations. These approaches could result in rapid information with high relevance from a user’s perspective. However, the introduction of such approaches requires an assessment of the role that National Statistical Institutes aspire to play in the era of Big Data.

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Home > Dissertations and Theses > Computational and Data Sciences (PhD) Dissertations

Computational and Data Sciences (PhD) Dissertations

Below is a selection of dissertations from the Doctor of Philosophy in Computational and Data Sciences program in Schmid College that have been included in Chapman University Digital Commons. Additional dissertations from years prior to 2019 are available through the Leatherby Libraries' print collection or in Proquest's Dissertations and Theses database.

Dissertations from 2024 2024

Advancement in In-Silico Drug Discovery from Virtual Screening Molecular Dockings to De-Novo Drug Design Transformer-based Generative AI and Reinforcement Learning , Dony Ang

A Novel Correction for the Multivariate Ljung-Box Test , Minhao Huang

Medical Image Analysis Based on Graph Machine Learning and Variational Methods , Sina Mohammadi

Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena , Sachi Perera

Global to Glocal: A Confluence of Data Science and Earth Observations in the Advancement of the SDGs , Rejoice Thomas

Dissertations from 2023 2023

Computational Analysis of Antibody Binding Mechanisms to the Omicron RBD of SARS-CoV-2 Spike Protein: Identification of Epitopes and Hotspots for Developing Effective Therapeutic Strategies , Mohammed Alshahrani

Integration of Computer Algebra Systems and Machine Learning in the Authoring of the SANYMS Intelligent Tutoring System , Sam Ford

Voluntary Action and Conscious Intention , Jake Gavenas

Random Variable Spaces: Mathematical Properties and an Extension to Programming Computable Functions , Mohammed Kurd-Misto

Computational Modeling of Superconductivity from the Set of Time-Dependent Ginzburg-Landau Equations for Advancements in Theory and Applications , Iris Mowgood

Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data , Krupa Nagori

Stochastic Processes and Multi-Resolution Analysis: A Trigonometric Moment Problem Approach and an Analysis of the Expenditure Trends for Diabetic Patients , Isaac Nwi-Mozu

Applications of Causal Inference Methods for the Estimation of Effects of Bone Marrow Transplant and Prescription Drugs on Survival of Aplastic Anemia Patients , Yesha M. Patel

Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis , Albert Pierce

Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients , Ehsan Yaghmaei

Dissertations from 2022 2022

Computational Approaches to Facilitate Automated Interchange between Music and Art , Rao Hamza Ali

Causal Inference in Psychology and Neuroscience: From Association to Causation , Dehua Liang

Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues , Hanna Lu

Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom , Sunil Ramchandani

Probing the Boundaries of Human Agency , Sook Mun Wong

Dissertations from 2021 2021

Predicting Eye Movement and Fixation Patterns on Scenic Images Using Machine Learning for Children with Autism Spectrum Disorder , Raymond Anden

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models , Alexander Barrett

Applications of Machine Learning to Facilitate Software Engineering and Scientific Computing , Natalie Best

Exploring Behaviors of Software Developers and Their Code Through Computational and Statistical Methods , Elia Eiroa Lledo

Assessing the Re-Identification Risk in ECG Datasets and an Application of Privacy Preserving Techniques in ECG Analysis , Arin Ghazarian

Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking , Nicholas LaHaye

Machine-Learning-Based Approach to Decoding Physiological and Neural Signals , Elnaz Lashgari

Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions , Justin Le

Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning , Shiva Lotfallahzadeh Barzili

Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures , Chelsea Parlett

Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data , Jianwei Zheng

Dissertations from 2020 2020

Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents , Steven Agajanian

Allocation of Public Resources: Bringing Order to Chaos , Lance Clifner

A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay , Sidy Danioko

A Computational and Experimental Examination of the FCC Incentive Auction , Logan Gantner

Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning , Kayleigh Hyde

Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations , Oluyemi Odeyemi

On Quantum Effects of Vector Potentials and Generalizations of Functional Analysis , Ismael L. Paiva

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability , Luciano Rodriguez

Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder , Natalia Rosenfield

Connecting the Dots for People with Autism: A Data-driven Approach to Designing and Evaluating a Global Filter , Viseth Sean

Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance , Christopher Watkins

Dissertations from 2019 2019

Contributions to Variable Selection in Complexly Sampled Case-control Models, Epidemiology of 72-hour Emergency Department Readmission, and Out-of-site Migration Rate Estimation Using Pseudo-tagged Longitudinal Data , Kyle Anderson

Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images , Justin J. Gapper

Estimating Auction Equilibria using Individual Evolutionary Learning , Kevin James

Employing Earth Observations and Artificial Intelligence to Address Key Global Environmental Challenges in Service of the SDGs , Wenzhao Li

Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique , Chloe Martin-King

Theses from 2017 2017

Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods , Michael Schwartz

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We have 87 big data PhD Projects, Programmes & Scholarships

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big data PhD Projects, Programmes & Scholarships

Data quality and cleaning in big data, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Funded fellowship opportunities in Big Data Analytics, Artificial Intelligence and Machine Learning

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Canada PhD Programme

A Canadian PhD usually takes 3-6 years. Programmes sometimes include taught classes and training modules followed by a comprehensive examination. You will then carry on to research your thesis, before presenting and defending your work. Programmes are usually offered in English, but universities in Québec and New Brunswick may teach in French.

Big data analytics and visualization for personalized Type 2 Diabetes treatment and management support

Using big data approaches to enhance fluid stewardship in intensive care unit, funded phd project (european/uk students only).

This project has funding attached for UK and EU students, though the amount may depend on your nationality. Non-EU students may still be able to apply for the project provided they can find separate funding. You should check the project and department details for more information.

Modelling the Impact of Diagnostic Pathways in Cancer and Cardiovascular Disease - University of Swansea (part of Health Data Research UK’s Big Data for Complex Disease Driver Programme)

Funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Big data and machine learning for urban energy and sustainability assessment and design

Big data modelling the knowledge economy, unravelling atrial fibrillation complexity: advanced statistics and machine learning integration of electrophysiology clinical data and big data sources, [artificial intelligence, biostatistics, machine learning, digital healthcare, funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Context-aware Dynamic Encoding for Efficient Big Data Processing

Epsrc phd studentship in: big data, network complexity and machine learning to deliver targeted pro-active dwds maintenance, ai to the rescue of climate change, modelling air quality for cleaner urban planning, repurposing and enriching cardiovascular risk prediction model to identify people at risk of cancer – ucl (part of health data research uk’s big data for complex disease driver programme), ontology and rule-based reasoning for intelligent manufacturing digital twin, fully funded 3-year ph.d. position in transportation engineering at the university of canterbury, new zealand, location tracking via mobile big data mining.

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Big Data Analytics (PhD)

Program at a glance.

• In State Tuition
• Out of State Tuition

Learn more about the cost to attend UCF.

Big Data Analytics will train researchers with a statistics background to analyze massive, structured or unstructured data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.

The program will provide a strong foundation in the major methodologies associated with Big Data Analytics such as predictive analytics, data mining, text analytics and statistical analysis with an interdisciplinary component that combines the strength of statistics and computer science. It will focus on statistical computing, statistical data mining and their application to business, social, and health problems complemented with ongoing industrial collaborations. The scope of this program is specialized to prepare data scientists and data analysts who will work with very large data sets using both conventional and newly developed statistical methods.

The Ph.D. in Big Data Analytics requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 30 credit hours of courses, 21 credit hours of restricted elective coursework, and 21 credit hours of dissertation research.

All Ph.D. students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a “B” (3.0) in all courses completed toward the degree and since admission to the program.

Statistical Colloquium Requirement - The department has a course, STA 7920 (Statistical Colloquium). This is a 0-credit course and should not impact your GPA. However, you will need at least 5 semesters of STA 7920 before you can graduate. With this course, you must attend the departmental colloquial.

Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree

Program Tracks/Options

• Statistics Track

Application Deadlines

• International

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University of central florida colleges.

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Enter your information below to receive more information about the Big Data Analytics (PhD) program offered at UCF.

Program Prerequisites

Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra. These prerequisites are undergraduate courses offered through the Math department.

Degree Requirements

• All Ph.D. students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.

Required Courses

• STA6106 - Statistical Computing I (3)
• STA6236 - Regression Analysis (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6246 - Linear Models (3)
• STA6107 - Statistical Computing II (3)
• STA6366 - Statistical Methodology for Data Science I (3)
• STA6367 - Statistical Methodology for Data Science II (3)
• STA7920 - Statistical Colloquium
• STA7348 - Bayesian Modeling and Computation (3)
• STA7722 - Statistical Learning Theory (3)
• STA7734 - Statistical Asymptotic Theory in Big Data (3)

Restricted Electives (at least 9 credit hours must be STA coursework)

• Other courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.
• STA6226 - Sampling Theory and Applications (3)
• STA6237 - Nonlinear Regression (3)
• STA6346 - Advanced Statistical Inference I (3)
• STA6347 - Advanced Statistical Inference II (3)
• STA6507 - Nonparametric Statistics (3)
• STA6662 - Statistical Methods for Industrial Practice (3)
• STA6705 - Data Mining Methodology III (3)
• STA6707 - Multivariate Statistical Methods (3)
• STA6709 - Spatial Statistics (3)
• STA6857 - Applied Time Series Analysis (3)
• STA7239 - Dimension Reduction in Regression (3)
• STA7719 - Survival Analysis (3)
• STA7935 - Current Topics in Big Data Analytics (3)
• CAP5610 - Machine Learning (3)
• CAP6307 - Text Mining I (3)
• CAP6315 - Social Media and Network Analysis (3)
• CAP6318 - Computational Analysis of Social Complexity (3)
• CAP6737 - Interactive Data Visualization (3)
• COP5537 - Network Optimization (3)
• COP6526 - Parallel and Cloud Computation (3)
• COP6616 - Multicore Programming (3)
• COT6417 - Algorithms on Strings and Sequences (3)
• COT6505 - Computational Methods/Analysis I (3)
• ECM6308 - Current Topics in Parallel Processing (3)
• EEL5825 - Machine Learning and Pattern Recognition (3)
• EEL6760 - Data Intensive Computing (3)
• FIL6146 - Screenplay Refinement (3)
• ESI6247 - Experimental Design and Taguchi Methods (3)
• ESI6358 - Decision Analysis (3)
• ESI6418 - Linear Programming and Extensions (3)
• ESI6609 - Industrial Engineering Analytics for Healthcare (3)
• ESI6891 - IEMS Research Methods (3)
• STA5825 - Stochastic Processes and Applied Probability Theory (3)
• COP6731 - Advanced Database Systems (3)
• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA5176 - Introduction to Biostatistics (3)
• STA5703 - Data Mining Methodology I (3)
• STA6223 - Conventional Survey Methods (3)
• STA6224 - Bayesian Survey Methods (3)
• STA6704 - Data Mining Methodology II (3)
• STA6714 - Data Preparation (3)
• MAP6195 - Mathematical Foundations for Massive Data Modeling and Analysis (3)
• MAP6197 - Mathematical Introduction to Deep Learning (3)
• COP5711 - Parallel and Distributed Database Systems (3)
• CNT5805 - Network Science (3)

Dissertation

• Earn at least 21 credits from the following types of courses: STA 7980 - Dissertation Research The student must select a dissertation adviser by the end of the first year. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours. The dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.

Examinations

• After passing candidacy, students will enroll into dissertation hours (STA7980) with their dissertation advisor. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor.

Qualifying Examination

• The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) and at the start of the spring term. The courses required to prepare for the examination are STA 6246, STA 6366, STA 6367, STA 6326, STA 6327 and STA 6236. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their second year and are expected to have completed the exam by the end of their second year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program. It is strongly recommended that the student select a dissertation adviser by the completion of 18 credit hours of course work, and it is strongly recommended that the student works with the dissertation adviser to form a dissertation committee within two semesters of passing the Qualifying Examination. To pass the exam, students need to pass all 4 parts. Students must take all 4 parts of the qualifying exam in their first attempt and must have completed all courses covered by the exam.

Candidacy Examination

• The candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program After passing the candidacy examination and meeting other requirements, the student can register for Doctoral Dissertation (STA7980). A minimum of 21 Doctoral Dissertation credit hours are required. The Candidacy Examination can be attempted after passing the qualifying examination. The Candidacy Examination must be completed within one years after passing the qualifying examination. A student must successfully pass the Candidacy Examination within at most two attempts.

Admission to Candidacy

• The following are required to be admitted to candidacy and enroll in dissertation hours. Completion of all coursework, except for dissertation hours Successful completion of the qualifying examination Successful completion of the candidacy examination including a written proposal and oral defense The dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars Submittal of an approved program of study

Masters Along the Way

• PhD Students can obtain their Master's degree in Statistics & Data Science - Data Science Track along the way to their PhD degree. To satisfy the requirements for the MS degree, the student must complete the following requirements: 1 - Complete the 24 hours of required courses for the MS degree - Data Science track. 2.- Complete 12 credit hours from the elective list for the MS degree - Data Science track, except STA 5205, STA 5505 and STA 5738. The student has the option of choosing between thesis option or non-thesis option.

Independent Learning

• As will all graduate programs, independent learning is an important component of the Big Data Analytics doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.

Grand Total Credits: 72

Application requirements, financial information.

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

• Which are the best tools and software for big data processing?
• Evaluate the security issues that face big data.
• An analysis of large-scale data for social networks globally.
• The influence of big data storage systems.
• The best platforms for big data computing.
• The relation between business intelligence and big data analytics.
• The importance of semantics and visualization of big data.
• Analysis of big data technologies for businesses.
• The common methods used for machine learning in big data.
• The difference between self-turning and symmetrical spectral clustering.
• The importance of information-based clustering.
• Evaluate the hierarchical clustering and density-based clustering application.
• How is data mining used to analyze transaction data?
• The major importance of dependency modeling.
• The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

• Discuss the privacy issues in big data.
• Evaluate the storage systems of scalable in big data.
• The best big data processing software and tools.
• Data mining tools and techniques are popularly used.
• Evaluate the scalable architectures for parallel data processing.
• The major natural language processing methods.
• Which are the best big data tools and deployment platforms?
• The best algorithms for data visualization.
• Analyze the anomaly detection in cloud servers
• The scrutiny normally done for the recruitment of big data job profiles.
• The malicious user detection in big data collection.
• Learning long-term dependencies via the Fourier recurrent units.
• Nomadic computing for big data analytics.
• The elementary estimators for graphical models.
• The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

• Evaluate the data mining process.
• The influence of the various dimension reduction methods and techniques.
• The best data classification methods.
• The simple linear regression modeling methods.
• Evaluate the logistic regression modeling.
• What are the commonly used theorems?
• The influence of cluster analysis methods in big data.
• The importance of smoothing methods analysis in big data.
• How is fraud detection done through AI?
• Analyze the use of GIS and spatial data.
• How important is artificial intelligence in the modern world?
• What is agile data science?
• Analyze the behavioral analytics process.
• Semantic analytics distribution.
• How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

• The difference between big data analytics and traditional data analytics methods.
• Why do you think the organization should think beyond the Hadoop hype?
• Does the size of the data matter more than how recent the data is?
• Is it true that bigger data are not always better?
• The debate of privacy and personalization in maintaining ethics in big data.
• The relation between data science and privacy.
• Do you think data science is a rebranding of statistics?
• Who delivers better results between data scientists and domain experts?
• According to your view, is data science dead?
• Do you think analytics teams need to be centralized or decentralized?
• The best methods to resource an analytics team.
• The best business case for investing in analytics.
• The societal implications of the use of predictive analytics within Education.
• Is there a need for greater control to prevent experimentation on social media users without their consent?
• How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

• The machine learning algorithms are used for fall recognition.
• The divergence and convergence of the internet of things.
• The reliable data movements using bandwidth provision strategies.
• How is big data analytics using artificial neural networks in cloud gaming?
• How is Twitter accounts classification done using network-based features?
• How is online anomaly detection done in the cloud collaborative environment?
• Evaluate the public transportation insights provided by big data.
• Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
• Discuss the current data lossless compression in the smart grid.
• How does online advertising traffic prediction helps in boosting businesses?
• How is the hyperspectral classification done using the multiple kernel learning paradigm?
• The analysis of large data sets downloaded from websites.
• How does social media data help advertising companies globally?
• Which are the systems recognizing and enforcing ownership of data records?
• The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

• The various errors and uncertainty in making data decisions.
• The application of big data on tourism.
• The automation innovation with big data or related technology
• The business models of big data ecosystems.
• Privacy awareness in the era of big data and machine learning.
• The data privacy for big automotive data.
• How is traffic managed in defined data center networks?
• Big data analytics for fault detection.
• The need for machine learning with big data.
• The innovative big data processing used in health care institutions.
• The money normalization and extraction from texts.
• How is text categorization done in AI?
• The opportunistic development of data-driven interactive applications.
• The use of data science and big data towards personalized medicine.
• The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

• The data-centric network of things.
• Big data management using artificial intelligence supply chain.
• The big data analytics for maintenance.
• The high confidence network predictions for big biological data.
• The performance optimization techniques and tools for data-intensive computation platforms.
• The predictive modeling in the legal context.
• Analysis of large data sets in life sciences.
• How to understand the mobility and transport modal disparities sing emerging data sources?
• How do you think data analytics can support asset management decisions?
• An analysis of travel patterns for cellular network data.
• The data-driven strategic planning for citywide building retrofitting.
• How is money normalization done in data analytics?
• Major techniques used in data mining.
• The big data adaptation and analytics of cloud computing.
• The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

• How is ultra-targeted marketing done?
• The transition of A/B testing from digital to offline.
• How can big data and A/B testing be done to win an election?
• Evaluate the use of A/B testing on big data
• Evaluate A/B testing as a randomized control experiment.
• How does A/B testing work?
• The mistakes to avoid while conducting the A/B testing.
• The most ideal time to use A/B testing.
• The best way to interpret results for an A/B test.
• The major principles of A/B tests.
• Evaluate the cluster randomization in big data
• The best way to analyze A/B test results and the statistical significance.
• How is A/B testing used in boosting businesses?
• The importance of data analysis in conversion research
• The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

• Assess the benefits and barriers of big data in the public sector.
• The best approach to smart city data ecosystems.
• The big analytics used for policymaking.
• Evaluate the smart technology and emergence algorithm bureaucracy.
• Evaluate the use of citizen scoring in public services.
• An analysis of the government administrative data globally.
• The public values are found in the era of big data.
• Public engagement on local government data use.
• Data analytics use in policymaking.
• How are algorithms used in public sector decision-making?
• The democratic governance in the big data era.
• The best business model innovation to be used in sustainable organizations.
• How does the government use the collected data from various sources?
• The role of big data for smart cities.
• How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

• Who uses big data analytics?
• Evaluate structure machine learning.
• Explain the whole deep learning process.
• Which are the best ways to manage platforms for enterprise analytics?
• Which are the new technologies used in data management?
• What is the importance of data retention?
• The best way to work with images is when doing research.
• The best way to promote research outreach is through data management.
• The best way to source and manage external data.
• Does machine learning improve the quality of data?
• Describe the security technologies that can be used in data protection.
• Evaluate token-based authentication and its importance.
• How can poor data security lead to the loss of information?
• How to determine secure data.
• What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

• Evaluate the 5G networks and IoT.
• Analyze the use of Artificial intelligence in the modern world.
• How do ultra-power IoT technologies work?
• Evaluate the adaptive systems and models at runtime.
• How have smart cities and smart environments improved the living space?
• The importance of the IoT-based supply chains.
• How does smart agriculture influence water management?
• The internet applications naming and identifiers.
• How does the smart grid influence energy management?
• Which are the best design principles for IoT application development?
• The best human-device interactions for the Internet of Things.
• The relation between urban dynamics and crowdsourcing services.
• The best wireless sensor network for IoT security.
• The best intrusion detection in IoT.
• The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

• The best cloud computing platforms for big data analytics.
• The parallel programming techniques for big data processing.
• The importance of big data models and algorithms in research.
• Evaluate the role of big data analytics for smart healthcare.
• How is big data analytics used in business intelligence?
• The best machine learning methods for big data.
• Evaluate the Hadoop programming in big data analytics.
• What is privacy-preserving to big data analytics?
• The best tools for massive big data processing
• IoT deployment in Governments and Internet service providers.
• How will IoT be used for future internet architectures?
• How does big data close the gap between research and implementation?
• What are the cross-layer attacks in IoT?
• The influence of big data and smart city planning in society.
• Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

• Which are the most used languages in big data?
• How is scala used in big data research?
• Is scala better than Java in big data?
• How is scala a concise programming language?
• How does the scala language stream process in real-time?
• Which are the various libraries for data science and data analysis?
• How does scala allow imperative programming in data collection?
• Evaluate how scala includes a useful REPL for interaction.
• Evaluate scala’s IDE support.
• The data catalog reference model.
• Evaluate the basics of data management and its influence on research.
• Discuss the behavioral analytics process.
• What can you term as the experience economy?
• The difference between agile data science and scala language.
• Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

• The biggest investment is in big data analysis.
• How are multi-cloud and hybrid settings deep roots?
• Why do you think machine learning will be in focus for a long while?
• Discuss in-memory computing.
• What is the difference between edge computing and in-memory computing?
• The relation between the Internet of things and big data.
• How will digital transformation make the world a better place?
• How does data analysis help in social network optimization?
• How will complex big data be essential for future enterprises?
• Compare the various big data frameworks.
• The best way to gather and monitor traffic information using the CCTV images
• Evaluate the hierarchical structure of groups and clusters in the decision tree.
• Which are the 3D mapping techniques for live streaming data.
• How does machine learning help to improve data analysis?
• Evaluate DataStream management in task allocation.
• How is big data provisioned through edge computing?
• The model-based clustering of texts.
• The best ways to manage big data.
• The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

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BIG DATA MASTER THESIS

“Big data” can be generally defined as large arrival of data volume, variety, and velocities of data resources which enable cost-effective, creative data analysis for improved insight as well as visualization. In practice, big data size is changing gradually. It must permit the diverse types of inputs to be completely assimilated and evaluated to assist us in drawing conclusions.  This article provides you deep insight into the Big Data master thesis where you can get to cover all aspects needed to do big data research and thesis work effectively.  Let us first start with understanding the various processes in big data, 

Big Data Processes

• Data acquisition
• Recording of information
• Annotations
• Data representation
• Feature extraction
• Data analytics
• Interpretation

What are big data analysis techniques? 

• Identifying data entities and redundancies
• Processing the missing and abnormal values
• Processing the skewness
• Standardization and discretization of data
• Constructing attributes

More particular applications of Big Data in real life can be found on our website from which you can better understand these processes. Big data master thesis is the most sought-after research assistance service in the world, with students and researchers from renowned universities rushing to us. We are able to deliver the most trustworthy and comprehensive research help in Big Data thanks to our updated technical team of professionals. Let us now discuss the recent research directions of big data

What are the current directions of Big Data analytics?

• Collecting transforming and analyzing big data with the support of data center
• Integrating information from multiple sources along with the distribution of data for the purpose of management and computation. 
• Utilising the attributes like machine learning mechanism and data mining for large scale analysis of data
• Tools of data visualization are used along with machine learning techniques
• The concerns of privacy and security in big data are handled efficiently using the statistical theory and big data sampling processes

You can reach out to our experts for any of these areas of big data research . Big data is widely regarded as the most important technological advancement in today’s digital world. Contact us if you’re looking for a vast repository of research-related data drawn from real-time big data platforms. Let us now look into ongoing research areas in big data

Our ongoing activities in big data

• Basic theory study, analysis, and development
• Working with advanced techniques, methods, and algorithms
• Developing advanced techniques based on the latest technologies in order to enhance the efficiency of big data applications and solve many big data issues
• Enabling research scholars and students from all over the world to interact with big data researchers and scientists to integrate new technologies to carve out innovations
• Advanced technologies and methodologies for being developed to solve potential problems in Big Data analytics

Despite the fact that Big Data appears to be a big topic that would require several books and programs to cover, our developers are now focusing on the fundamentals of Big Data so that students understand what else to think about when digging deep into Big Data algorithms and strategies . Let us now look into the major demands of big data

What are the requirements of big data models?

• Novel applications, techniques, and advanced solutions for creating a positive impact in big data research
• New big data model for real-time data analysis and processing with enhanced security features to ensure privacy and secrecy of data. 

Here are a few of the world’s top technical specialists who have been working with Big Data projects from their inception. Let us now discuss the significance of the research.

What is the purpose of a research project?

Every research work has its own significance. But each one cannot be implemented in the real world. Here are many assumptions, hypotheses, and establishments that have the capacities to be worked out beyond. Almost all science-based imaginations and fiction are becoming reality. The following are the important aspects in which privacy and security policies have to be given priority

• Agricultural, logistic and financial data
• Sensor, web, and city data
• Integration/fusion of data for decision making
• Mining and visualization of data
• Utilising big data analytics in real-time

        For instance, 

• Smart cities management 

We have experienced qualified and professional engineers and skilled writers who have earned world-class certification to provide you with full support in all of these areas. In Big data master thesis, we utilize a systematic plan to maintain proper shape and consistency in the language of the scholarly work. All of your ideas, points of view, and references will be organized logically. Let’s look at some massive data processing techniques now.

Real-time applications of Big Data analytics

• Data obtained from location and GPS
• Integrated personal information from satellite images
• Call data records
• Enables reliable tracking of location and proper recommendation of routes
• Most useful in routing drones for applications in military, emergency situations and identifying infections
• Location information
• Determining the mobility pattern across the globe for containment of infectious diseases and planning transportation
• Data on location and consumption pattern
• Data from a smart meter, history of usage, and gas status
• Promoting the use of green energy by increasing conservation
• Establishing use efficiency by predicting energy consumption rate
• Record of patients’ data and electronic health record
• Health history data, X-rays, and images
• Enhance the health monitoring purposes and used in studying patient’s immune response
• Recommendation of activity for maintaining physical health and elderly people
• Data on network signal strength and network user information
• Geolocation and sensor data
• Network log activities, video camera data, and weblogs
• It is used in effective network signaling and network dynamics prediction
• Management of networks and cell deployment data generation
• Log and social media data
• Product reviews, tweets, and blog posts
• User service recommendations which are effective and efficient
• Online surveys and questionnaires, ECG, EMG, and pulse rate
• Data sensings like gyroscopes, accelerometer, and magnetometer
• Utilising smartphones and other online network frameworks for collecting and analyzing data on a large scale
• Selection and review of products 
• Location and data buying behavioral analysis
• Reviews on customer products and help in analyzing product’s strengths and weaknesses

There are also many more important big data applications in real-time specific to the requirements. Speak with one of our technical specialists about the practices we implemented to improve the effectiveness of our Big Data programs . Because we stick to a zero plagiarism standard, our writers promise that there’ll be no duplication in the final edition of your thesis that we prepare. We guarantee a thorough grammatical verification, internal review, and on-time submission . Let us now discuss the integrated and upgraded big data methods in further detail.

What are the technologies used in Big Data analytics? 

• Data retrieval, mining, analytics, and distribution
• Massive parallelism, machine learnin g, and AI 
• High-speed networking and high-performance computation
• Hadoop, Spark-based big data analytics technologies 

For quantitative, analytical, theoretical, and coding platforms related to all these methodologies, you can approach us for great big data master thesis writing . Our professionals can explain everything about Big Data and answer all of your questions at once. Let’s now get into the different types of Big Data tools

Best Big Data Management Tools

NoSQL provides for non-relational database for the purpose of storing wearing and managing data that is both unstructured and structured.  It does not need normalization and application porting integration .  Computational overhead is reduced big data distribution across different hosts led by elastic scaling .  The following are the important NoSQL-based tools in managing big data storage systems. 

• It is a highly reliable system for storing large volumes of data with fault tolerance
• It is used in reading the data once and interpreting it for writing many times by consuming minimal storage

For the pros and cons of these tools, you can get in touch with us at any time. The following are the major tools in managing the big database

• It is one of the important Hadoop tools for enabling machine learning and real-time data processing
• The tool is significantly used in operations of reading and writing, batch processing, joining streams, node failure handling, and many more
• Inbuilt applications in Spark is used in implementing many common programming languages
• Summarising, analysis of queries and data with SQL interface is one of the biggest advantages provided by Apache hive
• It facilitates and helps in maintaining the writing with the use of approaches like indexing
• It provides a data storage facility by and column-based data 
• Large datasets storage that located at the top of HDFS 
• It provides for aggregating and analyzing datasets with multiple rows in a very less period
• Analysis of large generator datasets is made easier 
• It provides increased performance, throughput and the response time is also quick
• It is an RDBMS data import and export tool
• Time for processing data is reduced by providing a mechanism for computational offloading

Once you reach out to us, we will provide you with a huge amount of standard and very precise research data regarding the use of these tools. Let us now look into some of the important tools that are used in big data processing mechanisms, 

• For data extraction from and to Hadoop, the flume is used
• HDFS data streaming by easy to use and flexible framework leading to efficient aggregation
• It is an important tool used in handling streaming functions and batches
• It is a highly efficient real-time analysis tool used in Hadoop based distributed stream processing
• By using distributed snapshots this tool provides increased performance in data operation by enabling fault tolerance
• It also provides an integrated runtime environment for batch processing and data streaming applications
• Hadoop cluster job parallelization tool that works by enabling coordination and workflow
• Multiple job execution with fault tolerance is allowed by this tool
• It is also used in seamless job control in web service APIs
• It is an important tool used in job management computation and scheduling of resources
• It is a programming framework based on Hadoop used in batch processing
• It can store a huge volume of distributed data in a cost-effective manner and so its scalability is also very high
• It is a tool that provides a proper Framework for processing data which is used in defining the workflow
• It also gives execution steps using a proper acyclic graphical representation
• Its interface is very simple and can be used in very fast data processing applications
• Switching from the MapReduce platform is also enabled by this tool
• It is one of the important large data processing tools used in clustering, classifying, regression, collaborative filtration, segmenting, and statistical modeling applications
• It is useful in complementing applications that involve the use of distributed data mining
• This tool is used in Hadoop based allocation of resources and scheduling jobs
• Hadoop 2.0 mechanism forms the basis of this tool which is used in managing resources and metadata maintenance while at the same time tracking user data
• Efficient resource utilization by adding YARN into Hadoop and higher data availability is provided by this tool

Any Big Data system’s success is largely determined by its tools and algorithm. Algorithms are used to regulate, find, and build the cognitive models of a Big Data system . One of the most significant functions of Big Data algorithms is to extract valuable information and analyze them for arriving at results . As a reason, in order to write the best code and programming for your big data projects , you’ll need to expand your skills in all major programming languages. Let’s have a look at some of the most essential big data programming languages in this area.

Top 3 programming languages for Big Data analytics

• It is a general-purpose programming language that consists of a large number of open-source packages used for the following purposes
• Data modeling, pre-processing, mining, and computation
• Machine learning, analysis of network graphs, and processing natural languages
• It is a highly user-friendly and object-oriented programming language that is well known for its flexible and supportive aspects that allows it to integrate with various other platforms for big data processing like Apache spark
• It is one of the common open-source programming languages used in data analysis and visualization
• It is also highly significant in handling complicated data as it provides for efficient storage systems and performing vector operations
• It is useful in performing all the following popular data related functions in a more efficient manner
• Reading and writing data into the memory
• Data cleansing, storage, visualization, mining, and machine learning
• It is one of the important tools in carrying out big data analytics and processing
• Apache Spark provides for complicated app development platform using multiple programming languages with Java enabled virtual machine-based data processing
• It is used in scala supported big data processing, analysis, and management
• It enables simple, quick, inherently immutable applications which reduces highly threaded security in the same kind of languages

You can surely get full support on all these tools and programming languages from us . Our professionals usually offer utmost priority to all of the vital parts of these Big Data research fields so that consumers can pleasantly execute their exploration . Our writers are likewise extremely organized about following your institution’s formatting rules and norms. You can therefore experience our services more confidently. 

We are helping individuals to carve out customized big data systems for their enhancement. We have got qualified teams of research experts, writers, developers, engineers, and technical teams to assist you in all aspects of your big data master thesis. We will look into the important stages in master thesis writing

Main Stages of writing a master’s thesis

Writing the best thesis is one of the important aspects to showcase your field knowledge, talent, and innovation thus, in turn, attracting a huge volume of readers. In this regard, our expert writers have been providing all the necessary resources and support to our customers in writing one of the best thesis works in any big data master thesis topic . In the following, you can find some important aspects of a master thesis

• Choose one of the most interesting and recent topics
• Try to create a holistic proposal
• Utilise all the relevant resources to carry out the research
• Give utmost importance to proofreading, checking, and formatting
• Have brief talks and detailed discussions with your guide and mentor regarding the content

As a result, you may want the assistance of professionals in the subject in order to begin your Big Data Master Thesis. We have links with experts from the world’s best firms, institutes, and academics; therefore we are well-versed in the technical aspects of contemporary Big Data research. Hence you can have all your research needs to be met in one place. Let us now talk about some important thesis topics in big data,  

Top 6 Big Data Master Thesis Topics

• Data retrieval based on queries
• Social network sentiment analysis both offline and online
• Correlated big data analysis for protecting the privacy
• Preserving the privacy and ensuring the security of big data users
• Big spatial data similarity search
• Allocation of resources in Big Data System with elevated security awareness

These are some of the most popular and current study areas in the field of big data. For any type of research support, including PhD proposals, dissertation writing help , paper publishing, assignments, producing literature reviews, and big data master thesis, feel free to contact our developers. We are happy to help you.

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List of Best Research and Thesis Topic Ideas for Data Science in 2022

In an era driven by digital and technological transformation, businesses actively seek skilled and talented data science potentials capable of leveraging data insights to enhance business productivity and achieve organizational objectives. In keeping with an increasing demand for data science professionals, universities offer various data science and big data courses to prepare students for the tech industry. Research projects are a crucial part of these programs and a well- executed data science project can make your CV appear more robust and compelling. A  broad range of data science topics exist that offer exciting possibilities for research but choosing data science research topics can be a real challenge for students . After all, a good research project relies first and foremost on data analytics research topics that draw upon both mono-disciplinary and multi-disciplinary research to explore endless possibilities for real –world applications.

As one of the top-most masters and PhD online dissertation writing services , we are geared to assist students in the entire research process right from the initial conception to the final execution to ensure that you have a truly fulfilling and enriching research experience. These resources are also helpful for those students who are taking online classes .

By taking advantage of our best digital marketing research topics in data science you can be assured of producing an innovative research project that will impress your research professors and make a huge difference in attracting the right employers.

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Data science thesis topics

We have compiled a list of data science research topics for students studying data science that can be utilized in data science projects in 2022. our team of professional data experts have brought together master or MBA thesis topics in data science  that cater to core areas  driving the field of data science and big data that will relieve all your research anxieties and  provide a solid grounding for  an interesting research projects . The article will feature data science thesis ideas that can be immensely beneficial for students as they cover a broad research agenda for future data science . These ideas have been drawn from the 8 v’s of big data namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virility that provide interesting and challenging research areas for prospective researches  in their masters or PhD thesis . Overall, the general big data research topics can be divided into distinct categories to facilitate the research topic selection process.

• Security and privacy issues
• Cloud Computing Platforms for Big Data Adoption and Analytics
• Real-time data analytics for processing of image , video and text
• Modeling uncertainty

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Our top thesis writing experts are available 24/7 to assist you the right university projects. Whether its critical literature reviews to complete your PhD. or Master Levels thesis.

DATA SCIENCE PHD RESEARCH TOPICS

The article will also guide students engaged in doctoral research by introducing them to an outstanding list of data science thesis topics that can lead to major real-time applications of big data analytics in your research projects.

• Intelligent traffic control ; Gathering and monitoring traffic information using CCTV images.
• Asymmetric protected storage methodology over multi-cloud service providers in Big data.
• Leveraging disseminated data over big data analytics environment.
• Internet of Things.
• Large-scale data system and anomaly detection.

What makes us a unique research service for your research needs?

We offer all –round and superb research services that have a distinguished track record in helping students secure their desired grades in research projects in big data analytics and hence pave the way for a promising career ahead. These are the features that set us apart in the market for research services that effectively deal with all significant issues in your research for.

• Plagiarism –free ; We strictly adhere to a non-plagiarism policy in all our research work to  provide you with well-written, original content  with low similarity index   to maximize  chances of acceptance of your research submissions.
• Publication; We don’t just suggest PhD data science research topics but our PhD consultancy services take your research to the next level by ensuring its publication in well-reputed journals. A PhD thesis is indispensable for a PhD degree and with our premier best PhD thesis services that  tackle all aspects  of research writing and cater to  essential requirements of journals , we will bring you closer to your dream of being a PhD in the field of data analytics.
• Research ethics: Solid research ethics lie at the core of our services where we actively seek to protect the  privacy and confidentiality of  the technical and personal information of our valued customers.
• Research experience: We take pride in our world –class team of computing industry professionals equipped with the expertise and experience to assist in choosing data science research topics and subsequent phases in research including findings solutions, code development and final manuscript writing.
• Business ethics: We are driven by a business philosophy that‘s wholly committed to achieving total customer satisfaction by providing constant online and offline support and timely submissions so that you can keep track of the progress of your research.

Now, we’ll proceed to cover specific research problems encompassing both data analytics research topics and big data thesis topics that have applications across multiple domains.

Get Help from Expert Thesis Writers!

TheresearchGuardian.com providing expert thesis assistance for university students at any sort of level. Our thesis writing service has been serving students since 2011.

Multi-modal Transfer Learning for Cross-Modal Information Retrieval

Aim and objectives.

The research aims to examine and explore the use of CMR approach in bringing about a flexible retrieval experience by combining data across different modalities to ensure abundant multimedia data.

• Develop methods to enable learning across different modalities in shared cross modal spaces comprising texts and images as well as consider the limitations of existing cross –modal retrieval algorithms.
• Investigate the presence and effects of bias in cross modal transfer learning and suggesting strategies for bias detection and mitigation.
• Develop a tool with query expansion and relevance feedback capabilities to facilitate search and retrieval of multi-modal data.
• Investigate the methods of multi modal learning and elaborate on the importance of multi-modal deep learning to provide a comprehensive learning experience.

The Role of Machine Learning in Facilitating the Implication of the Scientific Computing and Software Engineering

• Evaluate how machine learning leads to improvements in computational APA reference generator tools and thus aids in  the implementation of scientific computing
• Evaluating the effectiveness of machine learning in solving complex problems and improving the efficiency of scientific computing and software engineering processes.
• Assessing the potential benefits and challenges of using machine learning in these fields, including factors such as cost, accuracy, and scalability.
• Examining the ethical and social implications of using machine learning in scientific computing and software engineering, such as issues related to bias, transparency, and accountability.

Trustworthy AI

The research aims to explore the crucial role of data science in advancing scientific goals and solving problems as well as the implications involved in use of AI systems especially with respect to ethical concerns.

• Investigate the value of digital infrastructures  available through open data   in  aiding sharing  and inter linking of data for enhanced global collaborative research efforts
• Provide explanations of the outcomes of a machine learning model  for a meaningful interpretation to build trust among users about the reliability and authenticity of data
• Investigate how formal models can be used to verify and establish the efficacy of the results derived from probabilistic model.
• Review the concept of Trustworthy computing as a relevant framework for addressing the ethical concerns associated with AI systems.

The Implementation of Data Science and their impact on the management environment and sustainability

The aim of the research is to demonstrate how data science and analytics can be leveraged in achieving sustainable development.

• To examine the implementation of data science using data-driven decision-making tools
• To evaluate the impact of modern information technology on management environment and sustainability.
• To examine the use of  data science in achieving more effective and efficient environment management
• Explore how data science and analytics can be used to achieve sustainability goals across three dimensions of economic, social and environmental.

Big data analytics in healthcare systems

The aim of the research is to examine the application of creating smart healthcare systems and   how it can   lead to more efficient, accessible and cost –effective health care.

• Identify the potential Areas or opportunities in big data to transform the healthcare system such as for diagnosis, treatment planning, or drug development.
• Assessing the potential benefits and challenges of using AI and deep learning in healthcare, including factors such as cost, efficiency, and accessibility
• Evaluating the effectiveness of AI and deep learning in improving patient outcomes, such as reducing morbidity and mortality rates, improving accuracy and speed of diagnoses, or reducing medical errors
• Examining the ethical and social implications of using AI and deep learning in healthcare, such as issues related to bias, privacy, and autonomy.

Large-Scale Data-Driven Financial Risk Assessment

The research aims to explore the possibility offered by big data in a consistent and real time assessment of financial risks.

• Investigate how the use of big data can help to identify and forecast risks that can harm a business.
• Categories the types of financial risks faced by companies.
• Describe the importance of financial risk management for companies in business terms.
• Train a machine learning model to classify transactions as fraudulent or genuine.

Scalable Architectures for Parallel Data Processing

Big data has exposed us to an ever –growing volume of data which cannot be handled through traditional data management and analysis systems. This has given rise to the use of scalable system architectures to efficiently process big data and exploit its true value. The research aims to analyses the current state of practice in scalable architectures and identify common patterns and techniques to design scalable architectures for parallel data processing.

• To design and implement a prototype scalable architecture for parallel data processing
• To evaluate the performance and scalability of the prototype architecture using benchmarks and real-world datasets
• To compare the prototype architecture with existing solutions and identify its strengths and weaknesses
• To evaluate the trade-offs and limitations of different scalable architectures for parallel data processing
• To provide recommendations for the use of the prototype architecture in different scenarios, such as batch processing, stream processing, and interactive querying

Robotic manipulation modelling

The aim of this research is to develop and validate a model-based control approach for robotic manipulation of small, precise objects.

• Develop a mathematical model of the robotic system that captures the dynamics of the manipulator and the grasped object.
• Design a control algorithm that uses the developed model to achieve stable and accurate grasping of the object.
• Test the proposed approach in simulation and validate the results through experiments with a physical robotic system.
• Evaluate the performance of the proposed approach in terms of stability, accuracy, and robustness to uncertainties and perturbations.
• Identify potential applications and areas for future work in the field of robotic manipulation for precision tasks.

Big data analytics and its impacts on marketing strategy

The aim of this research is to investigate the impact of big data analytics on marketing strategy and to identify best practices for leveraging this technology to inform decision-making.

• Review the literature on big data analytics and marketing strategy to identify key trends and challenges
• Conduct a case study analysis of companies that have successfully integrated big data analytics into their marketing strategies
• Identify the key factors that contribute to the effectiveness of big data analytics in marketing decision-making
• Develop a framework for integrating big data analytics into marketing strategy.
• Investigate the ethical implications of big data analytics in marketing and suggest best practices for responsible use of this technology.

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Take a review of different varieties of thesis topics and samples from our website TheResearchGuardian.com on multiple subjects for every educational level.

Platforms for large scale data computing: big data analysis and acceptance

To investigate the performance and scalability of different large-scale data computing platforms.

• To compare the features and capabilities of different platforms and determine which is most suitable for a given use case.
• To identify best practices for using these platforms, including considerations for data management, security, and cost.
• To explore the potential for integrating these platforms with other technologies and tools for data analysis and visualization.
• To develop case studies or practical examples of how these platforms have been used to solve real-world data analysis challenges.

Distributed data clustering

Distributed data clustering can be a useful approach for analyzing and understanding complex datasets, as it allows for the identification of patterns and relationships that may not be immediately apparent.

To develop and evaluate new algorithms for distributed data clustering that is efficient and scalable.

• To compare the performance and accuracy of different distributed data clustering algorithms on a variety of datasets.
• To investigate the impact of different parameters and settings on the performance of distributed data clustering algorithms.
• To explore the potential for integrating distributed data clustering with other machine learning and data analysis techniques.
• To apply distributed data clustering to real-world problems and evaluate its effectiveness.

Analyzing and predicting urbanization patterns using GIS and data mining techniques".

The aim of this project is to use GIS and data mining techniques to analyze and predict urbanization patterns in a specific region.

• To collect and process relevant data on urbanization patterns, including population density, land use, and infrastructure development, using GIS tools.
• To apply data mining techniques, such as clustering and regression analysis, to identify trends and patterns in the data.
• To use the results of the data analysis to develop a predictive model for urbanization patterns in the region.
• To present the results of the analysis and the predictive model in a clear and visually appealing way, using GIS maps and other visualization techniques.

Use of big data and IOT in the media industry

Big data and the Internet of Things (IoT) are emerging technologies that are transforming the way that information is collected, analyzed, and disseminated in the media sector. The aim of the research is to understand how big data and IoT re used to dictate information flow in the media industry

• Identifying the key ways in which big data and IoT are being used in the media sector, such as for content creation, audience engagement, or advertising.
• Analyzing the benefits and challenges of using big data and IoT in the media industry, including factors such as cost, efficiency, and effectiveness.
• Examining the ethical and social implications of using big data and IoT in the media sector, including issues such as privacy, security, and bias.
• Determining the potential impact of big data and IoT on the media landscape and the role of traditional media in an increasingly digital world.

Exigency computer systems for meteorology and disaster prevention

The research aims to explore the role of exigency computer systems to detect weather and other hazards for disaster prevention and response

• Identifying the key components and features of exigency computer systems for meteorology and disaster prevention, such as data sources, analytics tools, and communication channels.
• Evaluating the effectiveness of exigency computer systems in providing accurate and timely information about weather and other hazards.
• Assessing the impact of exigency computer systems on the ability of decision makers to prepare for and respond to disasters.
• Examining the challenges and limitations of using exigency computer systems, such as the need for reliable data sources, the complexity of the systems, or the potential for human error.

Network security and cryptography

Overall, the goal of research is to improve our understanding of how to protect communication and information in the digital age, and to develop practical solutions for addressing the complex and evolving security challenges faced by individuals, organizations, and societies.

• Developing new algorithms and protocols for securing communication over networks, such as for data confidentiality, data integrity, and authentication
• Investigating the security of existing cryptographic primitives, such as encryption and hashing algorithms, and identifying vulnerabilities that could be exploited by attackers.
• Evaluating the effectiveness of different network security technologies and protocols, such as firewalls, intrusion detection systems, and virtual private networks (VPNs), in protecting against different types of attacks.
• Exploring the use of cryptography in emerging areas, such as cloud computing, the Internet of Things (IoT), and blockchain, and identifying the unique security challenges and opportunities presented by these domains.
• Investigating the trade-offs between security and other factors, such as performance, usability, and cost, and developing strategies for balancing these conflicting priorities.

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These days the internet is being widely used than it was used a few years back. It has become a core part of our life. Billions of people are using social media and social networking every day all across the globe. Such a huge number of people generate a flood of data which have become quite complex to manage. Considering this enormous data, a term has been coined to represent it. So, what is this term called? Yes, Big Data Big Data is the term coined to refer to this huge amount of data. The concept of big data is fast spreading its arms all over the world. It is a trending topic for thesis, project, research, and dissertation. There are various good topics for the master’s thesis and research in Big Data and Hadoop as well as for Ph.D. First of all know, what is big data and Hadoop?

Find the link at the end to download the latest thesis and research topics in Big Data

What is Big Data?

Big Data refers to the large volume of data which may be structured or unstructured and which make use of certain new technologies and techniques to handle it. An organized form of data is known as structured data while an unorganized form of data is known as unstructured data. The data sets in big data are so large and complex that we cannot handle them using traditional application software. There are certain frameworks like Hadoop designed for processing big data. These techniques are also used to extract useful insights from data using predictive analysis, user behavior, and analytics. You can explore more on big data introduction while working on the thesis in Big Data. Big Data is defined by three Vs:

Volume – It refers to the amount of data that is generated. The data can be low-density, high volume, structured/unstructured or data with unknown value. This unknown data is converted into useful one using technologies like Hadoop. The data can range from terabytes to petabytes. Velocity – It refers to the rate at which the data is generated. The data is received at an unprecedented speed and is acted upon in a timely manner. It also requires real-time evaluation and action in case of the Internet of Things(IoT) applications. Variety – Variety refers to different formats of data. It may be structured, unstructured or semi-structured. The data can be audio, video, text or email. In this additional processing is required to derive the meaning of data and also to support the metadata. In addition to these three Vs of data, following Vs are also defined in big data. Value – Each form of data has some value which needs to be discovered. There are certain qualitative and quantitative techniques to derive meaning from data. For deriving value from data, certain new discoveries and techniques are required. Variability – Another dimension for big data is the variability of data i.e the flow of data can be high or low. There are challenges in managing this flow of data.

Thesis Research Topics in Big Data

• Privacy, Security Issues in Big Data .
• Storage Systems of Scalable for Big Data .
• Massive Big Data Processing of Software and Tools.
• Techniques and Data Mining Tools for Big Data .
• Big Data Adoptation and Analytics of Cloud Computing Platforms.
• Scalable Architectures for Parallel Data Processing.

Can you imagine how big is big data? Of course, you can’t. The amount of big data that is generated and stored on a global scale is unbelievable and is growing day by day. But do you know, only a small portion of this data is actually analyzed mainly for getting useful insights and information?

Big Data Hadoop

Hadoop is an open-source framework provided to process and store big data. Hadoop makes use of simple programming models to process big data in a distributed environment across clusters of computers. Hadoop provides storage for a large volume of data along with advanced processing power. It also gives the ability to handle multiple tasks and jobs.

Big Data Hadoop Architecture

HDFS is the main component of Hadoop architecture. It stands for Hadoop Distributed File Systems. It is used to store a large amount of data and multiple machines are used for this storage. MapReduce Overview is another component of big data architecture. The data is processed here in a distributed manner across multiple machines. YARN component is used for data processing resources like CPU, RAM, and memory. Resource Manager and Node Manager are the elements of YARN. These two elements work as master and slave. Resource Manager is the master and assigns resources to the slave i.e. Node Manager. Node Manager sends the signal to the master when it is going to start the work. Big Data Hadoop for the thesis will be plus point for you.

Importance of Hadoop in big data

Hadoop is essential especially in terms of big data . The importance of Hadoop is highlighted in the following points: Processing of huge chunks of data – With Hadoop, we can process and store huge amount of data mainly the data from social media and IoT(Internet of Things) applications. Computation power – The computation power of Hadoop is high as it can process big data pretty fast. Hadoop makes use of distributed models for processing of data. Fault tolerance – Hadoop provide protection against any form of malware as well as from hardware failure. If a node in the distributed model goes down, then other nodes continue to function. Copies of data are also stored. Flexibility – As much data as you require can be stored using Hadoop. There is no requirement of preprocessing the data. Low Cost – Hadoop is an open-source framework and free to use. It provides additional hardware to store the large quantities of data. Scalability – The system can be grown easily just by adding nodes in the system according to the requirements. Minimal administration is required.

Challenges of Hadoop

No doubt Hadoop is a very good platform for big data solution, still, there are certain challenges in this.

These challenges are:

• All problems cannot be solved – It is not suitable for iteration and interaction tasks. Instead, it is efficient for simple problems for which division into independent units can be made.
• Talent Gap – There is a lack of talented and skilled programmers in the field of MapReduce in big data especially at entry level.
• Security of data – Another challenge is the security of data. Kerberos authentication protocol has been developed to provide a solution to data security issues.
• Lack of tools – There is a lack of tools for data cleaning, management, and governance. Tools for data quality and standardization are also lacking.

Fields under Big Data

Big Data is a vast field and there are a number of topics and fields under it on which you can work for your thesis, dissertation as well as for research. Big Data is just an umbrella term for these fields.

Search Engine Data – It refers to the data stored in the search engines like Google, Bing and is retrieved from different databases. Social Media Data – It is a collection of data from social media platforms like Facebook, Twitter. Stock Exchange Data – It is a data from companies indulged into shares business in the stock market. Black box Data – Black Box is a component of airplanes, helicopters for voice recording of fight crew and for other metrics.

Big Data Technologies

Big Data technologies are required for more detailed analysis, accuracy and concrete decision making. It will lead to more efficiency, less cost, and less risk. For this, a powerful infrastructure is required to manage and process huge volumes of data.

The data can be analyzed with techniques like A/B Testing, Machine Learning, and Natural Language Processing.

The big data technologies include business intelligence, cloud computing, and databases.

The visualization of data can be done through the medium of charts and graphs.

Multi-dimensional big data can be handled through tensor-based computation. Tensor-based computation makes use of linear relations in the form of scalars and vectors. Other technologies that can be applied to big data are:

Massively Parallel Processing Search based applications Data Mining Distributed databases Cloud Computing

These technologies are provided by vendors like Amazon, Microsoft, IBM etc to manage the big data.

MapReduce Algorithm for Big Data

A large amount of data cannot be processed using traditional data processing approaches. This problem has been solved by Google using an algorithm known as the MapReduce algorithm. Using this algorithm, the task can be divided into small parts and these parts are assigned to distributed computers connected on the network. The data is then collected from individual computers to form a final dataset.

The MapReduce algorithm is used by Hadoop to run applications in which parallel processing of data is done on different nodes. Hadoop framework can develop applications that can run on clusters of computers to perform statistical analysis of a large amount of data.

The MapReduce algorithm consist of two tasks: Map Reduce

A set is of data is taken by Map which is converted into another set of data in which individual elements are broken into pairs known as tuples. Reduce takes the output of Map task as input. It combines data tuples into smaller tuples set.

The MapReduce algorithm is executed in three stages: Map Shuffle Reduce

In the map stage, the input data is processed and stored in the Hadoop file system(HDFS). After this a mapper performs the processing of data to create small chunks of data. Shuffle stage and Reduce stage occur in combination. The Reducer takes the input from the mapper for processing to create a new set of output which will later be stored in the HDFS. The Map and Reduce tasks are assigned to appropriate servers in the cluster by the Hadoop. The Hadoop framework manages all the details like issuing of tasks, verification, and copying. After completion, the data is collected at the Hadoop server. You can get thesis and dissertation guidance for the thesis in Big Data Hadoop from data analyst.

Applications of Big Data

Big Data find its application in various areas including retail, finance, digital media, healthcare, customer services etc.

Big Data is used within governmental services with efficiency in cost, productivity, and innovation. The common example of this is the Indian Elections of 2014 in which BJP tried this to win the elections. The data analysis, in this case, can be done by the collaboration between the local and the central government. Big Data was the major factor behind Barack Obama’s win in the 2012 election campaign.

Big Data is used in finance for market prediction. It is used for compliance and regulatory reporting, risk analysis, fraud detection, high-speed trading and for analytics. The data which is used for market prediction is known as alternate data.

Big Data is used in health care services for clinical data analysis, disease pattern analysis, medical devices and medicines supply, drug discovery and various other such analytics. Big Data analytics have helped in a major way in improving the healthcare systems. Using these certain technologies have been developed in healthcare systems like eHealth, mHealth, and wearable health gadgets.

Media uses Big Data for various mechanisms like ad targeting, forecasting, clickstream analytics, campaign management and loyalty programs. It is mainly focused on following three points:

Targeting consumers Capturing of data Data journalism

Big Data is a core of IoT(Internet of Things) . They both work together. Data can be extracted from IoT devices for mapping which helps in interconnectivity. This mapping can be used to target customers and for media efficiency by the media industry.

Information Technology

Big Data has helped employees working in Information Technology to work efficiently and for widespread distribution of Information Technology. Certain issues in Information Technology can also be resolved using Big Data. Big Data principles can be applied to machine learning and artificial intelligence for providing better solutions to the problems.

Advantages of Big Data

Big Data has certain advantages and benefits, particularly for big organizations.

• Time Management – Big data saves valuable time as rather than spending hours on managing the different amount of data, big data can be managed efficiently and at a faster pace.
• Accessibility – Big Data is easily accessible through authorization and data access rights and privileges.
• Trustworthy – Big Data is trustworthy in the sense that we can get valuable insights from the data.
• Relevant – The data is relevant whereas irrelevant data require filtering which can lead to complexity.
• Secure – The data is secured using data hosting and through various advanced technologies and techniques.

Challenges of Big Data

Although Big Data has come in a big way in improving the way we store data, there are certain challenges which need to be resolved.

• Data Storage and quality of Data – The data is growing at a fast pace as the number of companies and organizations are growing. Proper storage of this data has become a challenge. This data can be stored in data warehouses but this data is inconsistent. There are issues of errors, duplicacy, conflicts while storing this data in their native format. Moreover, this changes the quality of data.
• Lack of big data analysts – There is a huge demand for data scientists and analysts who can understand and analyze this data. But there are very few people who can work in this field considering the fact that huge amount of data is produced every day. Those who are there don’t have proper skills.
• Quality Analysis – Big companies and organizations use big for getting useful insights to make proper decisions for future plans. The data should also be accurate as inaccurate data can lead to wrong decisions that will affect the company business. Therefore quality analysis of the data should be there. For this testing is required which is a time-consuming process and also make use of expensive tools.
• Security and Privacy of Data – Security, and privacy are the biggest risks in big data. The tools that are used for analyzing, storing, managing use data from different sources. This makes data vulnerable to exposure. It increases security and privacy concerns.

Thus Big Data is providing a great help to companies and organizations to make better decisions. This will ultimately lead to more profit. The main thesis topics in Big Data and Hadoop include applications, architecture, Big Data in IoT, MapReduce, Big Data Maturity Model etc.

Latest Thesis and Research Topics in Big Data

There are a various thesis and research topics in big data for M.Tech and Ph.D. Following is the list of good topics for big data for masters thesis and research:

Big Data Virtualization

Internet of Things(IoT)

Big Data Maturity Model

Data Science

Data Federation

Big Data Analytics

SQL-on-Hadoop

Predictive Analytics

Big Data Virtualization is the process of creating virtual structures rather than actual for Big Data systems. It is very beneficial for big enterprises and organizations to use their data assets to achieve their goals and objectives. Virtualization tools are available to handle big data analytics.

Big Data and IoT work in coexistence with each other. IoT devices capture data which is extracted for connectivity of devices. IoT devices have sensors to sense data from its surroundings and can act according to its surrounding environment.

Big Data Maturity Models are used to measure the maturity of big data. These models help organizations to measure big data capabilities and also assist them to create a structure around that data. The main goal of these models is to guide organizations to set their development goals.

Data Science is more or less related to Data Mining in which valuable insights and information are extracted from data both structured and unstructured. Data Science employs techniques and methods from the fields of mathematics, statistics, and computer science for processing.

Data Federation is the process of collecting data from different databases without copying and without transferring the original data. Rather than whole information, data federation collects metadata which is the description of the structure of original data and keep them in a single database.

Sampling is a technique of statistics to find and locate patterns in Big Data. Sampling makes it possible for the data scientists to work efficiently with a manageable amount of data. Sampled data can be used for predictive analytics. Data can be represented accurately when a large sample of data is used.

It is the process of exploring large datasets for the sake of finding hidden patterns and underlying relations for valuable customer insights and other useful information. It finds its application in various areas like finance, customer services etc. It is a good choice for Ph.D. research in big data analytics.

Clustering is a technique to analyze big data. In clustering, a group of similar objects is grouped together according to their similarities and characteristics. In other words, this technique partitions the data into different sets. The partitioning can be hard partitioning and soft partitioning. There are various algorithms designed for big data and data mining. It is a good area for thesis and researh in big data.

SQL-on-Hadoop is a methodology for implementing SQL on Hadoop platform by combining together the SQL-style querying system to the new components of the Hadoop framework. There are various ways to execute SQL in Hadoop environment which include – connectors for translating the SQL into a MapReduce format, push down systems to execute SQL in Hadoop clusters, systems that distribute the SQL work between MapReduce – HDFS clusters and raw HDFS clusters. It is a very good topic for thesis and research in Big Data.

It is a technique of extracting information from the datasets that already exist in order to find out the patterns and estimate future trends. Predictive Analytics is the practical outcome of Big Data and Business Intelligence(BI). There are predictive analytics models which are used to get future insights. For this future insight, predictive analytics take into consideration both current and historical data. It is also an interesting topic for thesis and research in Big Data.

These were some of the good topics for big data for M.Tech and masters thesis and research work. For any help on thesis topics in Big Data, contact Techsparks . Call us on this number 91-9465330425  or email us at [email protected] for M.Tech and Ph.D. help in big data thesis topics.

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Latest Thesis and Research Topics in Big Data(pdf)

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• Big Data Analytics Projects for Engineering Students

Big Data Analytics Projects for Engineering Students that are highly utilized which reveals the models, trends in extensive data are shared by us in this page. Along with recommended measures and anticipated outcomes, we provide numerous compelling projects in the domain of big data analytics.

• Real-Time Traffic Analysis and Prediction

This research intends to enhance traffic directions and decrease blockages by assessing and anticipating traffic patterns in real-time through modeling an advanced system.

Significant Mechanisms:

• Especially for data storage, deploy Hadoop HDFS.
• For data streaming, use Apache Kafka.
• Apply Python for analysis and visualization.
• Implement Apache Spark for real-time data processing.
• Data Collection: From GPS devices and sensors, we must gather real-time traffic data.
• Data Streaming: To a processing application, stream the data by using Apache Kafka.
• Real-Time Processing: For the purpose of processing and evaluating the data, Spark Streaming ought to be implemented.
• Prediction Framework: Predict traffic jams by creating a predictive framework with the aid of machine learning techniques.
• Visualization: In order to exhibit real-time traffic conditions and anticipations, a dashboard needs to be designed.

Anticipated Outcome:

• Considering traffic flow and blockage, this study can offer real-time visualization.
• It crucially facilitates dynamic traffic management through offering predictive perceptions into traffic models.
• Predictive Maintenance for Industrial Equipment

Our project aims to anticipate breakdowns by evaluating sensor data from industrial appliances. Before the occurrence of failures, plan the maintenance services.

• For machine learning, execute Python with Scikit-learn.
• Use Power BI or Tableau for visualization.
• Deploy Apache Spark for data processing.
• Implement Hadoop for data storage.
• Data Collection: Particularly from industrial devices, past records of sensor data have to be collected.
• Data Preprocessing: To manage missing values and noisy data, the data has to be cleaned and preprocessed.
• Feature Engineering: Appropriate characteristics must be retrieved such as consumption patterns, vibration and temperature.
• Model Building: For forecasting the breakdowns of equipment, a machine learning framework like SVM (Support Vector Machine) and Random Forest is required to be trained.
• Visualization: As a means to observe the health condition of equipment, create efficient dashboards. The maintenance requirements should be anticipated by us.
• Probable breakdowns of equipment could be detected initially.
• By means of predictive perspectives, interruptions and expenses on maintenance are decreased.
• Smart Energy Consumption Analysis

In smart grids, detect models and enhance energy consumption by assessing the data of energy usage.

• For analysis and visualization, utilize Python.
• Use Apache Spark for batch processing.
• Regarding dashboards, execute Power BI.
• Apply Hadoop for big data storage.
• Data Collection: Energy usage data is required to be accumulated from sensors and smart meters.
• Data Synthesization: To collect and handle extensive amounts of data, Apache Hadoop should be utilized.
• Data Analysis: For evaluating usage patterns and identifying outliers, implement Spark.
• Development Model: Anticipate energy requirements and enhance energy supply by creating frameworks.
• Visualization: Exhibit directions of energy consumption through modeling responsive dashboards and recommend efficient tactics for developments.
• Energy usage patterns and outliers can be detected.
• Expenses on energy are decreased and capability of energy distribution could be enhanced, as a result of this research.
• Healthcare Analytics for Patient Outcomes

It is intended to anticipate patient results by evaluating EHRs (Electronic Health Records). Development of healthcare services is the key focus of this research.

• To perform data processing, utilize Apache Spark.
• For analysis, make use of Python with Pandas and Scikit-learn.
• Take advantage of Tableau for data visualization.
• Carry out data processing by using Apache Spark.
• Data Collection: Especially from hospitals, we should gather health data and medical records of patients.
• Data Cleaning: To manage discrepancies and missing values, the data has to be preprocessed.
• Feature Selection: Crucial properties which affect patients results are meant to be detected.
• Model Development: Forecast the patient results such as rehabilitation or re-admission, we must acquire the benefit of machine learning frameworks.
• Visualization: As a means to display patient data and anticipation outcome, dashboard is supposed to be developed by us.
• This research paves the way for authentic anticipation of health susceptibilities and patient results.
• By means of data-based perspectives, decision-making processes can be improved for assisting healthcare service providers.
• E-commerce Customer Segmentation

According to the purchasing activities, classify the consumers through evaluating the data of user purchase. This research mainly focuses on development of marketing tactics.

• Perform clustering and analysis with the help of python.
• Use Power BI for visualization.
• Carry out data storage by using Hadoop.
• For data processing, implement Apache Spark.
• Data Collection: From an e-commerce environment, transaction data needs to be collected.
• Data Cleaning: In order to manage missing values, the data has to be cleaned and preprocessed.
• Feature Engineering: Characteristics such as market price, originality and incidence must be developed.
• Clustering: To classify consumers, we need to execute clustering techniques such as K-means.
• Visualization: For exhibiting user classification and consumer behavior patterns, an effective dashboard should be designed.
• This study could facilitate the detection of various consumer segments.
• In order to focus efficiently on various consumer groups, customized marketing tactics could be developed.
• Social Media Sentiment Analysis

Regarding items, programs or brands, social media data is meant to be evaluated for assessing the people’s sentiment.

• Specifically for text analysis, use Python with NLP libraries such as NLTK.
• Make use of Tableau for visualization.
• To conduct real-time data processing, apply Apache Spark.
• Data Collection: By using APIs from Facebook or Twitter, accumulate posts of social media.
• Data Processing: The text data is required to be cleaned and preprocessed with the application of Spark.
• Sentiment Analysis: To categorize sentiments as positive, negative or impartial, we have to execute NLP (Natural Language Processing) methods.
• Analysis of Directions: Regarding people sentiment, directions and models of research should be detected.
• Visualization: For visualizing sentiment directions and findings of analysis, we need to model productive dashboards.
• Considering the sentiment and patterns, a real-time analysis can be offered through this research.
• To assist in decision-making and strategy development, it offers innovative perspectives into public perspectives of brands or programs.
• Financial Fraud Detection

With the aid of big data analytics, the illegal payments need to be identified in financial datasets.

• For outlier detection, use Python with machine learning libraries.
• Utilize Power BI for dashboards.
• Perform data processing by using Apache Spark.
• Apply Hadoop for data storage,
• Data Collection: From financial entities, transaction data should be collected.
• Data Preprocessing: The data has to be cleaned and standardized.
• Feature Engineering: Characteristics which reflect illegal behaviors are supposed to be detected.
• Model Development: To identify outliers and diminish the probable frauds, machine learning frameworks must be trained.
• Visualization: An efficient dashboard is required to be created for the purpose of observing the payments and identifying the unauthentic or illegal activities.
• Unauthentic or fraudulent payments could be identified initially.
• As a result of real-time monitoring and alert messages, security can be enhanced and economic losses are decreased.
• Climate Data Analysis for Environmental Monitoring

To observe ecological modifications and anticipate future directions, our project evaluates the extensive climate data.

• By using python, perform analysis and visualization.
• Implement Tableau for dashboards.
• Use Hadoop for data storage.
• For data processing, deploy Apache Spark.
• Data Collection: Climate data needs to be gathered from ecological databases and sensors.
• Data Synthesization: Use Hadoop to accumulate and synthesize the data.
• Data Analysis: Considering the climate data, we have to assess directions and models by implementing Spark.
• Predictive Modeling: To anticipate upcoming ecological modifications, an efficient model ought to be created.
• Visualization: For exhibiting climate directions and forecastings, we must design dashboards.
• By means of this research, climate trends and models can be detected.
• Particularly for climate change reduction, ecological monitoring and dynamic standards are optimized.
• Retail Sales Forecasting

      Forecast upcoming sales and enhance stock management through assessing the historical data of sales.

• Employ Python for time series analysis and predictions.
• Apply Hadoop for data storage.
• By using Power BI, conduct visualization.
• Use Apache Spark for data processing.
• Data Collection: From retail industries, past records of sales data ought to be collected.
• Data Cleaning: For managing missing values and seasonal changes, the data must be cleaned and pre-processed.
• Feature Engineering: It is approachable to develop characteristics such as holidays, sales directions and advancements.
• Model Building: Specifically for predicting, we should make use of time series frameworks such as LSTM or ARIMA.
• Visualization: An effective dashboard must be created for displaying the sales predictions and directions.
• To aid stock accessibility, exact prediction of sales could be anticipated.
• As regards predictive perceptions, maintenance schedule and resource utilization are enhanced.
• IoT Data Analytics for Smart Agriculture

In order to improve resource allocation and crop productivity, the data is intended to be evaluated from IoT sensors in agriculture.

• With the application of Tableau, carry out visualization.
• For data processing, utilize Apache Spark.
• Acquire the benefit of Python for analysis and machine learning.
• Data Collection: On the basis of rainfall, soil texture and temperature, we should gather data from IoT sensors.
• Data Synthesization: By using Hadoop, we must accumulate and synthesize the data.
• Data Analysis: To evaluate sensor data and detect models, implement Spark.
• Predictive Modeling: Forecast the crop productivity and enhance resource consumption through modeling efficient frameworks,
• Visualization: Track the agricultural parameters and anticipations by designing dashboards.
• With the help of data-based decisions, resource capability and crop productivity can be optimized.
• For dynamic management, this project contributes real-time monitoring of agricultural scenarios.

What are some projects for data analysis and Python as a beginner to help my proficiency?

As a beginner, you must focus on considerable problems before getting started with a project. To develop your skills in handling these issues, some of the general research challenges which involved in data analysis are offered by us that are accompanied with appropriate and probable findings:

• Problem: Managing Missing Data

Explanation:

Generally in datasets, missing data is a general issue which results in mitigation of model authenticity and provides partial findings.

Probable Findings:

• Imputation Methods: We should make use of imputation techniques such as mean, median, or mode imputation or more complicated methods like MICE (Multiple Imputation by Chained Equations) or KNN (K-Nearest Neighbors).
• Removal: Even though it results in lack of significant details, records with missing values ought to be eliminated.
• Model-Based Imputation: On the basis of various characteristics, evaluate missing values by using predictive frameworks.
• Problem: Imbalanced Datasets

In a classification issue, when one class surpasses the others in a substantial manner it can bring about biased frameworks which extensively support the broad groups.

• Resampling Methods: To stabilize the classes, we have to implement oversampling methods like SMOTE or undersampling methods.
• Cost-Effective Learning: Regarding the smallest groups, rectify the classification errors by changing the learning techniques.
• Anomaly Detection Techniques: Less privileged class ought to be considered as an outlier. To detect these anomalies, employ techniques of anomaly detection.
• Problem: Overfitting in Machine Learning Models

When a model functions more effectively on training data than hidden data, overfitting problems could be presented which reflects that, instead of interpreting the fundamental patterns, it understands the noisy data.

• Regularization: As a means to rectify the extensive complicated frameworks, deploy L1 or L2 regularization methods.
• Cross-Validation: For assuring the model, whether it simplifies efficiently, implement methods such as k-fold cross-validation.
• Simpler Models: We should begin with modest frameworks and if it is required, we can progressively move on to complicated models.
• Pruning: This pruning method eliminates the unnecessary or lack of important branches in decision trees to decrease overadaptation.
• Problem: Data Privacy Concerns

Specifically with expansive regulation standards such as GDPR, data which contains sensible details are evaluated, as it brings about critical secrecy problems.

• Data Anonymization: From datasets, we have to eliminate or overshadow the PII ( Personally Identifiable Information ).
• Differential Privacy: While still accessing the beneficial analysis, secure the personal secrecy through executing productive methods which incorporate noise to the data.
• Federated Learning: Focus on training models on decentralized data, in which only the model upgrades are distributed and the data occupies a position of local devices.
• Problem: High Dimensionality

      The problems in dimensionality give rise to high-dimensional data which involves several characteristics. As the amount of dimensions expands, frameworks become less productive.

• Dimensionality Mitigation: To decrease the extensive characteristics, we have to deploy methods such as t-SNE or PCA (Principal Component Analysis).
• Feature Selection: By using techniques such as RFE (Recursive Feature Elimination), the most significant characteristics are supposed to be detected and preserved.
• Standardization: For decreasing the implications of crucial characteristics, regularization methods such as L1 (Lasso) must be executed.
• Problem: Data Drift in Real-Time Systems

This gives rise to reduction in model functionality (data drift) due to the statistical features of data which are deployed by a model in a periodical approach.

• Model Monitoring: It is required to observe the functionality of a model in a consistent manner. At the time of corruption, we should identify it, as it reflects possible implications.
• Retraining Models: Apply the most advanced data which adapts to modifications to retrain the framework eventually.
• Drift Detection Techniques: If it is required, activate retraining by executing the drift identification techniques such as adaptive windowing or Page-Hinkley test.
• Problem: Interpretability of Complex Models

      Most of the models such as deep learning networks are considered as “black boxes”. It could be challenging to interpret the process of developing specific decisions.

• Model Explainability Methods: To offer perceptions into model forecastings, deploy techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations.
• Apply Simpler Techniques: Apply more explainable tools such as linear regression or decision trees, as regarding the crucial cases of intelligibility.
• Visualization Tools: Particularly for assistance in understanding the process of model’s decision-making, visualization tools are meant to be modeled
• Problem: Scalability of Data Processing

Conventional data processing and analysis methods can become unworkable or ineffective due to the size expansion of datasets.

• Distributed Computing: As a means to operate extensive datasets in an effective manner, employ distributed computing models such as Apache Spark.
• Data Sampling: When entire scale processing is not possible, we must collaborate with a proportional sample of data.
• Effective Techniques: For extensive data, create or implement advanced techniques which are designed specifically.
• Problem: Bias in Data and Algorithms

Especially in risky areas such as law enforcement or hiring, biases are included in datasets and techniques which result in inequitable or unauthentic results.

• Bias Identification: In both data and model forecastings, we should identify and evaluate bias by executing significant tools.
• Bias Reduction: To decrease unfairness in models, utilize methods such as adversarial debiasing, re-sampling and re-weighting.
• Extensive Data Collection: For decreasing the intrinsic unfairness, the data collection process is supposed to be assured, if it is as extensive as possible.
• Problem: Real-Time Data Analysis and Decision Making

Regarding the case of requirement for high authenticity and minimal response time, it can be difficult to assess and make decisions in accordance with real-time data.

• Stream Processing: To manage real-time data, stream processing models need to be executed such as Apache Flink or Apache Kafka.
• Real-Time Model Assumption: For real-time conclusion with minimal response time, we have to implement advanced and effective frameworks.
• Edge Computing: In order to process data nearer to its origin, acquire the benefit of edge computing which decreases the bandwidth consumption and response time.

Big Data Analytics Thesis for Engineering Students

Big Data Analytics Thesis for Engineering Students that are rapidly growing in the various field with innovative methods, novel strategies, plans and effective techniques are listed by phdtopic.com. our writers will clearly explain the prevalent and promising areas in big data analytics we also share with you the critical problems with suitable solutions. Get your article writing done perfectly from our well experienced writers.

• Research on Architecture of Power Big Data High-Speed Storage System for Energy Interconnection
• Application Research of VCR Model Based on AHP in the Design of Government Service Big Data Scenario
• Data quality in big data processing: Issues, solutions and open problems
• The design and implementation of the enterprise level data platform and big data driven applications and analytics
• Study on information recommendation of scientific and technological achievements based on user behavior modeling and big data mining
• Research on System Design of Big Data-driven Teachers’ Professional Ability Development
• Big Data Forecasting Model of Indoor Positions for Mobile Robot Navigation Based on Apache Spark Platform
• Research on the Teaching Reform of Finance and Accounting Major under the Background of Big Data
• Research on identification technology of encircled serial tags based on big data and semantic analysis
• Application research of Big data in provincial important Product traceability system
• Right to Digital Privacy: A Technological Intervention of Blockchain and Big Data Analytics
• A Data Replenishment Method for Self Regulating Sail-Assisted Ships Considering Big Data
• Research on Hotspot and Trend of Online Public Opinion Research in Big Data Environment
• Research and implementation of big data preprocessing system based on Hadoop
• Analysis and Improvement Strategy for Profit Contribution of Bank Customer Under Big Data Background
• Research on the Application of Big Data and Artificial Intelligence Technology in Computer Network Technology
• Business information modeling: A methodology for data-intensive projects, data science and big data governance
• A Big Data Storage Scheme Based on Distributed Storage Locations and Multiple Authorizations
• Application of remote sensing big data technology in refined urban management
• Research on Campus Convergent Payment System Model Based on Blockchain Big Data Algorithm

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• PhD Thesis on Big Data Analytics

PhD Thesis on Big Data Analytics is a thesis link where PhD scholars can hold their unique thesis in the latest trend. In big data analytics, thesis completion is a big thing for PhD beginners. Since it is a complex process due to the variety of data analysis, in this case, our PhD thesis on Big Data Analytics is the best solution for it.

To deal with the BIG DATA, you will need our BIG SERVICE. It translates our insights to the research scholars over 15+ years….

Assets of our PhD Thesis on Big Data analytics

• R&D team with enthusiastic techies update all latest research tendencies
• Skilled research analysts with in-depth knowledge in every aspect of big data
• Striving to break all prior researches with our innovative approaches
• Self-motivated technical writers to write your thesis within your time boundary
• As an illustration, access to all academic databases

  We served the 1000+ thesis for PhD scholars in the field of Big Data without any error. You just plan the format of your thesis with your university or supervisor. If you do not have the format, no problem, we can customize your thesis according to our best structure.

From our experience, we will know that every PhD scholar will trap their thesis due to its  page length and reference count  matters.  Without good exposure, one couldn’t satisfy both constraints, especially in big data.

Are you trapping with your thesis? Bind with us; we will be your healing agent…

Understand our Big Data analytics

Big data can probably define in the 3V’s, but today we will find the big data is a 10v’s. At the present time, it is described in all the fields. Besides, it will change the entire world into smart cities. As a matter of fact, most of the scholars will prefer big data. Now, let’s check those terms (10v’s) in big data,

• Variability
• Vulnerability
• Visualization

Our Big Data Analytics tools for your research

Considering the current trend in big data, we will make the team for it. In addition, our experts have 18 years of field practice in several trends. Furthermore, our project experts are experts and skill in all the data tools. So we are capable of working on both the big data models and tools offering PhD thesis on big data analytics .

For example,

• Hadoop with AWS
• Microsoft Azure
• Google Cloud Platform

 Our momentous data processing tools are,

• Apache Hadoop
• Spark Apache
• Apache Storm
• Apache SAMOA

Here fewer auspicious thesis topics in big data are enlisted,

• Elastic cloud/fog design
• Best practices for data acquisition, fusion, and cleaning
• Big data visualization
• New programming model beyond Hadoop/MapReduce, STORM
• Large scale recommender system
• Statistical, Neural, Reinforcement, and multi-learning strategies
• Automated data extraction and semantic processing
• Data security using blockchain technology
• Interoperability of heterogeneous data in the cloud platform
• Big data analytics in industrial environments

To sum up, start your PhD thesis with us to save your precious time because you deserve it… A little progress each day adds up to big results…

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

• Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
• Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.

I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.

It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.

My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.

I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.

- Christopher

Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.

I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.

You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.

These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.

Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.

I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.

Trusted customer service that you offer for me. I don’t have any cons to say.

I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!

- Abdul Mohammed

Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.

I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.

I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!

- Bhanuprasad

I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.

- Ghulam Nabi

I am extremely happy with your project development support and source codes are easily understanding and executed.

Hi!!! You guys supported me a lot. Thank you and I am 100% satisfied with publication service.

- Abhimanyu

I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!

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