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Seven case studies in carbon and climate

Every part of the mosaic of Earth's surface — ocean and land, Arctic and tropics, forest and grassland — absorbs and releases carbon in a different way. Wild-card events such as massive wildfires and drought complicate the global picture even more. To better predict future climate, we need to understand how Earth's ecosystems will change as the climate warms and how extreme events will shape and interact with the future environment. Here are seven pressing concerns.

The Far North is warming twice as fast as the rest of Earth, on average. With a 5-year Arctic airborne observing campaign just wrapping up and a 10-year campaign just starting that will integrate airborne, satellite and surface measurements, NASA is using unprecedented resources to discover how the drastic changes in Arctic carbon are likely to influence our climatic future.

Wildfires have become common in the North. Because firefighting is so difficult in remote areas, many of these fires burn unchecked for months, throwing huge plumes of carbon into the atmosphere. A recent report found a nearly 10-fold increase in the number of large fires in the Arctic region over the last 50 years, and the total area burned by fires is increasing annually.

Organic carbon from plant and animal remains is preserved for millennia in frozen Arctic soil, too cold to decompose. Arctic soils known as permafrost contain more carbon than there is in Earth's atmosphere today. As the frozen landscape continues to thaw, the likelihood increases that not only fires but decomposition will create Arctic atmospheric emissions rivaling those of fossil fuels. The chemical form these emissions take — carbon dioxide or methane — will make a big difference in how much greenhouse warming they create.

Initial results from NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) airborne campaign have allayed concerns that large bursts of methane, a more potent greenhouse gas, are already being released from thawing Arctic soils. CARVE principal investigator Charles Miller of NASA's Jet Propulsion Laboratory (JPL), Pasadena, California, is looking forward to NASA's ABoVE field campaign (Arctic Boreal Vulnerability Experiment) to gain more insight. "CARVE just scratched the surface, compared to what ABoVE will do," Miller said.

Methane is the Billy the Kid of carbon-containing greenhouse gases: it does a lot of damage in a short life. There's much less of it in Earth's atmosphere than there is carbon dioxide, but molecule for molecule, it causes far more greenhouse warming than CO 2 does over its average 10-year life span in the atmosphere.

Methane is produced by bacteria that decompose organic material in damp places with little or no oxygen, such as freshwater marshes and the stomachs of cows. Currently, over half of atmospheric methane comes from human-related sources, such as livestock, rice farming, landfills and leaks of natural gas. Natural sources include termites and wetlands. Because of increasing human sources, the atmospheric concentration of methane has doubled in the last 200 years to a level not seen on our planet for 650,000 years.

Locating and measuring human emissions of methane are significant challenges. NASA's Carbon Monitoring System is funding several projects testing new technologies and techniques to improve our ability to monitor the colorless gas and help decision makers pinpoint sources of emissions. One project, led by Daniel Jacob of Harvard University, used satellite observations of methane to infer emissions over North America. The research found that human methane emissions in eastern Texas were 50 to 100 percent higher than previous estimates. "This study shows the potential of satellite observations to assess how methane emissions are changing," said Kevin Bowman, a JPL research scientist who was a coauthor of the study.

Tropical forests

Tropical forests are carbon storage heavyweights. The Amazon in South America alone absorbs a quarter of all carbon dioxide that ends up on land. Forests in Asia and Africa also do their part in "breathing in" as much carbon dioxide as possible and using it to grow.

However, there is evidence that tropical forests may be reaching some kind of limit to growth. While growth rates in temperate and boreal forests continue to increase, trees in the Amazon have been growing more slowly in recent years. They've also been dying sooner. That's partly because the forest was stressed by two severe droughts in 2005 and 2010 — so severe that the Amazon emitted more carbon overall than it absorbed during those years, due to increased fires and reduced growth. Those unprecedented droughts may have been only a foretaste of what is ahead, because models predict that droughts will increase in frequency and severity in the future.

In the past 40-50 years, the greatest threat to tropical rainforests has been not climate but humans, and here the news from the Amazon is better. Brazil has reduced Amazon deforestation in its territory by 60 to 70 percent since 2004, despite troubling increases in the last three years. According to Doug Morton, a scientist at NASA's Goddard Space Flight Center in Greenbelt, Maryland, further reductions may not make a marked difference in the global carbon budget. "No one wants to abandon efforts to preserve and protect the tropical forests," he said. "But doing that with the expectation that [it] is a meaningful way to address global greenhouse gas emissions has become less defensible."

In the last few years, Brazil's progress has left Indonesia the distinction of being the nation with the highest deforestation rate and also with the largest overall area of forest cleared in the world. Although Indonesia's forests are only a quarter to a fifth the extent of the Amazon, fires there emit massive amounts of carbon, because about half of the Indonesian forests grow on carbon-rich peat. A recent study estimated that this fall, daily greenhouse gas emissions from recent Indonesian fires regularly surpassed daily emissions from the entire United States.

Wildfires are natural and necessary for some forest ecosystems, keeping them healthy by fertilizing soil, clearing ground for young plants, and allowing species to germinate and reproduce. Like the carbon cycle itself, fires are being pushed out of their normal roles by climate change. Shorter winters and higher temperatures during the other seasons lead to drier vegetation and soils. Globally, fire seasons are almost 20 percent longer today, on average, than they were 35 years ago.

Currently, wildfires are estimated to spew 2 to 4 billion tons of carbon into the atmosphere each year on average — about half as much as is emitted by fossil fuel burning. Large as that number is, it's just the beginning of the impact of fires on the carbon cycle. As a burned forest regrows, decades will pass before it reaches its former levels of carbon absorption. If the area is cleared for agriculture, the croplands will never absorb as much carbon as the forest did.

As atmospheric carbon dioxide continues to increase and global temperatures warm, climate models show the threat of wildfires increasing throughout this century. In Earth's more arid regions like the U.S. West, rising temperatures will continue to dry out vegetation so fires start and burn more easily. In Arctic and boreal ecosystems, intense wildfires are burning not just the trees, but also the carbon-rich soil itself, accelerating the thaw of permafrost, and dumping even more carbon dioxide and methane into the atmosphere.

North American forests

With decades of Landsat satellite imagery at their fingertips, researchers can track changes to North American forests since the mid-1980s. A warming climate is making its presence known.

Through the North American Forest Dynamics project, and a dataset based on Landsat imagery released this earlier this month, researchers can track where tree cover is disappearing through logging, wildfires, windstorms, insect outbreaks, drought, mountaintop mining, and people clearing land for development and agriculture. Equally, they can see where forests are growing back over past logging projects, abandoned croplands and other previously disturbed areas.

"One takeaway from the project is how active U.S. forests are, and how young American forests are," said Jeff Masek of Goddard, one of the project’s principal investigators along with researchers from the University of Maryland and the U.S. Forest Service. In the Southeast, fast-growing tree farms illustrate a human influence on the forest life cycle. In the West, however, much of the forest disturbance is directly or indirectly tied to climate. Wildfires stretched across more acres in Alaska this year than they have in any other year in the satellite record. Insects and drought have turned green forests brown in the Rocky Mountains. In the Southwest, pinyon-juniper forests have died back due to drought.

Scientists are studying North American forests and the carbon they store with other remote sensing instruments. With radars and lidars, which measure height of vegetation from satellite or airborne platforms, they can calculate how much biomass — the total amount of plant material, like trunks, stems and leaves — these forests contain. Then, models looking at how fast forests are growing or shrinking can calculate carbon uptake and release into the atmosphere. An instrument planned to fly on the International Space Station (ISS), called the Global Ecosystem Dynamics Investigation (GEDI) lidar, will measure tree height from orbit, and a second ISS mission called the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) will monitor how forests are using water, an indicator of their carbon uptake during growth. Two other upcoming radar satellite missions (the NASA-ISRO SAR radar, or NISAR, and the European Space Agency’s BIOMASS radar) will provide even more complementary, comprehensive information on vegetation.

Ocean carbon absorption

When carbon-dioxide-rich air meets seawater containing less carbon dioxide, the greenhouse gas diffuses from the atmosphere into the ocean as irresistibly as a ball rolls downhill. Today, about a quarter of human-produced carbon dioxide emissions get absorbed into the ocean. Once the carbon is in the water, it can stay there for hundreds of years.

Warm, CO 2 -rich surface water flows in ocean currents to colder parts of the globe, releasing its heat along the way. In the polar regions, the now-cool water sinks several miles deep, carrying its carbon burden to the depths. Eventually, that same water wells up far away and returns carbon to the surface; but the entire trip is thought to take about a thousand years. In other words, water upwelling today dates from the Middle Ages – long before fossil fuel emissions.

That's good for the atmosphere, but the ocean pays a heavy price for absorbing so much carbon: acidification. Carbon dioxide reacts chemically with seawater to make the water more acidic. This fundamental change threatens many marine creatures. The chain of chemical reactions ends up reducing the amount of a particular form of carbon — the carbonate ion — that these organisms need to make shells and skeletons. Dubbed the “other carbon dioxide problem,” ocean acidification has potential impacts on millions of people who depend on the ocean for food and resources.

Phytoplankton

Microscopic, aquatic plants called phytoplankton are another way that ocean ecosystems absorb carbon dioxide emissions. Phytoplankton float with currents, consuming carbon dioxide as they grow. They are at the base of the ocean's food chain, eaten by tiny animals called zooplankton that are then consumed by larger species. When phytoplankton and zooplankton die, they may sink to the ocean floor, taking the carbon stored in their bodies with them.

Satellite instruments like the Moderate resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua let us observe ocean color, which researchers can use to estimate abundance — more green equals more phytoplankton. But not all phytoplankton are equal. Some bigger species, like diatoms, need more nutrients in the surface waters. The bigger species also are generally heavier so more readily sink to the ocean floor.

As ocean currents change, however, the layers of surface water that have the right mix of sunlight, temperature and nutrients for phytoplankton to thrive are changing as well. “In the Northern Hemisphere, there’s a declining trend in phytoplankton,” said Cecile Rousseaux, an oceanographer with the Global Modeling and Assimilation Office at Goddard. She used models to determine that the decline at the highest latitudes was due to a decrease in abundance of diatoms. One future mission, the Pre-Aerosol, Clouds, and ocean Ecosystem (PACE) satellite, will use instruments designed to see shades of color in the ocean — and through that, allow scientists to better quantify different phytoplankton species.

In the Arctic, however, phytoplankton may be increasing due to climate change. The NASA-sponsored Impacts of Climate on the Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) expedition on a U.S. Coast Guard icebreaker in 2010 and 2011 found unprecedented phytoplankton blooms under about three feet (a meter) of sea ice off Alaska. Scientists think this unusually thin ice allows sunlight to filter down to the water, catalyzing plant blooms where they had never been observed before.

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• Published: 21 April 2022

The case for data science in experimental chemistry: examples and recommendations

• Junko Yano   ORCID: orcid.org/0000-0001-6308-9071 1 ,
• Kelly J. Gaffney   ORCID: orcid.org/0000-0002-0525-6465 2 , 3 ,
• John Gregoire   ORCID: orcid.org/0000-0002-2863-5265 4 ,
• Linda Hung   ORCID: orcid.org/0000-0002-1578-6152 5 ,
• Abbas Ourmazd   ORCID: orcid.org/0000-0001-9946-3889 6 ,
• Joshua Schrier   ORCID: orcid.org/0000-0002-2071-1657 7 ,
• James A. Sethian   ORCID: orcid.org/0000-0002-7250-7789 8 , 9 &
• Francesca M. Toma   ORCID: orcid.org/0000-0003-2332-0798 10  

Nature Reviews Chemistry volume  6 ,  pages 357–370 ( 2022 ) Cite this article

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The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to ‘co-design’ chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

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Acknowledgements

This article evolved from presentations and discussions at the workshop ‘At the Tipping Point: A Future of Fused Chemical and Data Science’ held in September 2020, sponsored by the Council on Chemical Sciences, Geosciences, and Biosciences of the US Department of Energy, Office of Science, Office of Basic Energy Sciences. The authors thank the members of the Council for their encouragement and assistance in developing this workshop. In addition, the authors are indebted to the agencies responsible for funding their individual research efforts, without which this work would not have been possible.

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Yano, J., Gaffney, K.J., Gregoire, J. et al. The case for data science in experimental chemistry: examples and recommendations. Nat Rev Chem 6 , 357–370 (2022). https://doi.org/10.1038/s41570-022-00382-w

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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Case Studies in Science Education

A video library for k-8 science teachers: 25 half-hour video programs and guides.

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Making Learning Relevant With Case Studies

The open-ended problems presented in case studies give students work that feels connected to their lives.

To prepare students for jobs that haven’t been created yet, we need to teach them how to be great problem solvers so that they’ll be ready for anything. One way to do this is by teaching content and skills using real-world case studies, a learning model that’s focused on reflection during the problem-solving process. It’s similar to project-based learning, but PBL is more focused on students creating a product.

Case studies have been used for years by businesses, law and medical schools, physicians on rounds, and artists critiquing work. Like other forms of problem-based learning, case studies can be accessible for every age group, both in one subject and in interdisciplinary work.

You can get started with case studies by tackling relatable questions like these with your students:

• How can we limit food waste in the cafeteria?
• How can we get our school to recycle and compost waste? (Or, if you want to be more complex, how can our school reduce its carbon footprint?)
• How can we improve school attendance?
• How can we reduce the number of people who get sick at school during cold and flu season?

Addressing questions like these leads students to identify topics they need to learn more about. In researching the first question, for example, students may see that they need to research food chains and nutrition. Students often ask, reasonably, why they need to learn something, or when they’ll use their knowledge in the future. Learning is most successful for students when the content and skills they’re studying are relevant, and case studies offer one way to create that sense of relevance.

Teaching With Case Studies

Ultimately, a case study is simply an interesting problem with many correct answers. What does case study work look like in classrooms? Teachers generally start by having students read the case or watch a video that summarizes the case. Students then work in small groups or individually to solve the case study. Teachers set milestones defining what students should accomplish to help them manage their time.

During the case study learning process, student assessment of learning should be focused on reflection. Arthur L. Costa and Bena Kallick’s Learning and Leading With Habits of Mind gives several examples of what this reflection can look like in a classroom: 

Journaling: At the end of each work period, have students write an entry summarizing what they worked on, what worked well, what didn’t, and why. Sentence starters and clear rubrics or guidelines will help students be successful. At the end of a case study project, as Costa and Kallick write, it’s helpful to have students “select significant learnings, envision how they could apply these learnings to future situations, and commit to an action plan to consciously modify their behaviors.”

Interviews: While working on a case study, students can interview each other about their progress and learning. Teachers can interview students individually or in small groups to assess their learning process and their progress.

Student discussion: Discussions can be unstructured—students can talk about what they worked on that day in a think-pair-share or as a full class—or structured, using Socratic seminars or fishbowl discussions. If your class is tackling a case study in small groups, create a second set of small groups with a representative from each of the case study groups so that the groups can share their learning.

4 Tips for Setting Up a Case Study

1. Identify a problem to investigate: This should be something accessible and relevant to students’ lives. The problem should also be challenging and complex enough to yield multiple solutions with many layers.

2. Give context: Think of this step as a movie preview or book summary. Hook the learners to help them understand just enough about the problem to want to learn more.

3. Have a clear rubric: Giving structure to your definition of quality group work and products will lead to stronger end products. You may be able to have your learners help build these definitions.

4. Provide structures for presenting solutions: The amount of scaffolding you build in depends on your students’ skill level and development. A case study product can be something like several pieces of evidence of students collaborating to solve the case study, and ultimately presenting their solution with a detailed slide deck or an essay—you can scaffold this by providing specified headings for the sections of the essay.

Problem-Based Teaching Resources

There are many high-quality, peer-reviewed resources that are open source and easily accessible online.

• The National Center for Case Study Teaching in Science at the University at Buffalo built an online collection of more than 800 cases that cover topics ranging from biochemistry to economics. There are resources for middle and high school students.
• Models of Excellence , a project maintained by EL Education and the Harvard Graduate School of Education, has examples of great problem- and project-based tasks—and corresponding exemplary student work—for grades pre-K to 12.
• The Interdisciplinary Journal of Problem-Based Learning at Purdue University is an open-source journal that publishes examples of problem-based learning in K–12 and post-secondary classrooms.
• The Tech Edvocate has a list of websites and tools related to problem-based learning.

In their book Problems as Possibilities , Linda Torp and Sara Sage write that at the elementary school level, students particularly appreciate how they feel that they are taken seriously when solving case studies. At the middle school level, “researchers stress the importance of relating middle school curriculum to issues of student concern and interest.” And high schoolers, they write, find the case study method “beneficial in preparing them for their future.”

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Cautionary Tales: Ethics and Case Studies in Science

Ethical concerns are normally avoided in science classrooms in spite of the fact that many of our discoveries impinge directly on personal and societal values. We should not leave the ethical problems for another day, but deal with them using realistic case studies that challenge students at their ethical core. In this article we illustrate how case studies can be used to teach STEM students principles of ethics.

INTRODUCTION

Americans consider morality the most essential part of self ( 11 ).

This may be true of other cultures as well. All societies have elaborate rules of conduct that are often codified into law. Some of these imperatives seem hardwired. Human infants younger than a year and a half will look longer at visual displays showing violations of social rules ( 2 ). It is part of our primate heritage; individuals are punished if they stray far from acceptable behavior. Capuchin monkeys will reject a reward if they think they are being treated unfairly; they have a clear sense of right and wrong which depends on the social situation ( 3 ). Aesop would agree—he penned many a story where animals behaved badly and paid the penalty.

If morality and ethics are so central to our beings, what are our responsibilities as STEM educators to pass along the standards of society? And if we accept this challenge, what is the best way to instruct our youthful comrades in their quest for knowledge? I argue in this article that we should accept this obligation and that case study teaching is an ideal way to deliver the message.

Case-study teaching has a long and honorable lineage ( 4 ). In academic circles we find it used 100 years ago in Harvard Law School. The instructor would bring in a true criminal or civil case that had been adjudicated and conduct a class discussion with future lawyers, asking them to justify the rationale for the final decision—challenging them every step of the way. This provided students a real-world problem as part of their training for a real world ahead. The method was soon adopted by the Harvard Business School and various schools across the country, where it is now the standard. Medical schools have their own version of the method called Problem-Based Learning. Again the idea was to use real world problems to train physicians, but in this case students work in small groups to analyze patient problems and provide diagnoses. The idea of using similar strategies to teach basic sciences to undergraduates is largely due to the efforts of faculty at the University of Delaware and the National Center for Case Study Teaching in Science, where there are hundreds of cases now published http://sciencecases.lib.buffalo.edu/cs/ .

Research has shown that minorities and women undergraduates respond well to cases ( 5 , 8 ). Among this group, cases have been shown to increase students’ understanding of science by encouraging them to make connections between science concepts and situations they may encounter in their lives ( 7 ). In addition, the case method promotes the internalization of learning and the development of analytical and decision-making skills, as well as proficiency in oral communication and teamwork ( 6 ). The method, moreover, is a flexible teaching tool. Cases can take many different forms and be taught in many different ways, ranging from the classical discussion method used in business and law schools, to the arguably strongest approaches, Problem-Based Learning and the Interrupted Case Method, with their emphasis on small-group, cooperative learning strategies ( 4 ).

The method seems ideal for teaching ethics to STEM students. We have plenty of precedents to guide us. We have legal ethics, business ethics, medical ethics, bioethics, geoethics, environmental ethics, teaching ethics, research ethics, engineering ethics, and so on. And, of course, there are religious ethics, with each faith describing canons of behavior not to be breached. Some of them are commonly held community values, such as “thou shalt not steal, lie, or cheat.” Others are more specific, such as the research tenet, “thou should replicate experiments.” While some of these “rules” are so entrenched that they are tantamount to absolutes, others are more fragile and malleable; they are subject to the changing moral landscape. Policies about smoking in public places have rapidly shifted ( 12 ). Decrees against interracial marriage, once laws of the land, are now anachronisms, as are statues against same-sex marriage ( 1 , 10 ). Such shifts in the moral topography offer wonderful opportunities for case studies as they challenge students at their central core of beliefs. There are hundreds of these case studies now available for teachers in repositories such as the National Center for Case Study Teaching in Science ( http://sciencecases.lib.buffalo.edu ), where you can find moral dilemmas depicted in cases on evolution, genetic engineering, nutrition, euthanasia, cloning, and organic farming.

Case studies can be used to show students acceptable standards of behavior within a given profession—the do’s and don’ts—and the disastrous consequences that can occur if the rules are not obeyed. We learn of breaches of research ethics such as fraud, plagiarism, and sloppy book-keeping that ruin careers. We come to know cautionary tales, like Dr. Andrew Wakefield, who misrepresented the medical histories of 12 patients and claimed that his research results showed that vaccinations caused autism. He was eventually discredited and Britain stripped him of his medical license. Unfortunately, this sensational allegation has resulted in thousands of people refusing to have their children vaccinated, with a subsequent striking rise in measles.

In the past, these stories were neglected in the STEM classroom. Questions of right or wrong belonged elsewhere—in the home, in a philosophy class, in a church or tabernacle. In the science classroom we learned how to make petroleum, shoot rockets, synthesize drugs, manipulate DNA, and clone animals, not whether we should do so. Then came the Second World War. The academic community ran squarely into two striking examples of the deep entanglement of science and ethics. Suddenly, there was a public debate about whether Truman’s decision to drop the atom bomb on Japan with the loss of millions of lives was ethical. The sensational trials of generals and scientists implicated in the atrocities at the Nazi concentration camps came to light during the Nuremberg Trials and patient bills of rights were drafted. Today our IRB committees and other ethical bodies monitor our experimental protocols involving research into issues of genetic engineering, stem cell research, three-parent embryos, etc. So my argument is that we should not ignore these disputes in the science classroom; this is where the technology is coming from—the STEM laboratories and the people in charge.

This is especially true as scientists have gained technological expertise; we see more clearly than ever how science and technological decisions can wreak havoc in our lives. Think about science in the courtroom, the public policy decisions on health and insurance, the intrusion of listening devices and the tracking of our e-mails and phone calls, the science of warfare and the use of chemical weapons and drones, the use of chemical fertilizers and organic farming, and possible designer babies. Very little that we humans do is not filled with moral or ethical conundrums. No more should we eschew these quandaries in our classrooms. When we discuss DNA genomes, we should not only speak of how the technology can be used to track potential criminals, but also how it can lead to social and personal dilemmas when we identify parentage, plot evolutionary lineages, discover genetically modified food, and detect mutations that might lead to lethal disease and the loss of insurance. How better to deal with such contentious matters than to use case studies? Case studies are stories with an educational message, and as such they are perfect vehicles to integrate science with societal and policy issues. They are ideal because of their interdisciplinary nature. They deal with real issues that students will face in the future. And people love stories.

RESOURCES FOR ETHICS CASES

There are several STEM case repositories in the world; arguably the largest is the National Center for Case Study Teaching in Science, with over 500 case studies published over the past 25 years. Its greatest strength is in the fields of biology and health-related professions. Over 100 cases are catalogued as having ethical issues, ranging in suitability from middle school student classes to faculty seminars.

We seldom find pure instances of ethical transgressions, where issues of fraud, fabrication, or plagiarism are discussed. Rather, ethical issues are more apt to be a sidebar to the main thrust of a case concentrated on a health or environmental problem. And even in these cases, an individual may not be wrestling with problems involving societal standards. Instead, they grapple with whether it is prudent to make one decision versus another. It may be as simple as whether or not to have an operation or whether it is healthy to use drugs to lose weight.

Let me give you a flavor of the kinds of issues and cases that are available:

Personal dilemma

Often such cases involve medical issues, as we see in “A Right to Her Genes” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=316&id=316 ). In this true story, students examine the case of a woman, Michelle, with a family predisposition to cancer, who is considering genetic testing. The woman wishes to get some information to confirm this predisposition from a reluctant aunt so that she can better decide whether to remove her breasts and/or ovaries prophylactically. The aunt is illiterate and poor and had previously been estranged from the rest of the family. A genetic counselor is involved to help educate the aunt and hopefully obtain consent to get a DNA sample from her. Michelle must decide for herself what course of action she should take.

In “Spirituality and Health Care: A Request for Prayer” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=434&id=434 ), a fourth-year medical student making hospital rounds with an attending physician is asked by a family member of a patient to pray with her. The case allows medical students to explore issues related to patients’ religious beliefs as they think through how they might respond to different expectations and requests they may receive from patients and their families in their professional career.

Social ethics

These are cases where protagonists must decide how they will respond to evolving social standards. “SNPs and Snails and Puppy Dog Tails, and That’s What People Are Made Of” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=337&id=337 ) deals with questions of genome privacy. Students work together to research six lobbying groups’ views in this area and then present their insights before a mock meeting of a U.S. House of Representatives Subcommittee voting on the Genetic Information Nondiscrimination Act. In working through the case, students learn about single nucleotide polymorphisms, common molecular biology techniques, and current legislation governing genome privacy.

“A Case of Cheating?” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=399&id=399 ) involves two international students who are accused of cheating at the end of the semester, and the teacher must decide how to handle the accusation so that all students see that justice is done. The case raises cultural questions in the context of the use of peer evaluation and cooperative learning strategies.

Medical ethics

Patient rights are a common concern in medical cases, whether they are the central issue of the case or a sidebar to teaching students about a particular disease syndrome. It is the central theme of the infamous “Bad Blood” case involving black men in Tuskegee, Alabama, in the 1920s ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=371&id=371 ). They had contracted syphilis, and public health officials studying the progress of the debilitating disease originally did not have an effective treatment. Twenty years later, the antibiotic penicillin was discovered, yet treatment was withheld to maintain the integrity of the study, whose purpose was to follow the progression of the disease. The study was immediately stopped when this transgression was made public.

Often there are competing concerns, as when a person is confronted with a decision where their personal morality may be at odds with the decrees of a society or institution. “The Plan: Ethics and Physician Assisted Suicide” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=436&id=436 ) is based on an article published in 1991 in the New England Journal of Medicine in which Dr. Timothy E. Quill described his care for a patient suffering from acute leukemia, including how he prescribed a lethal dose of barbiturates knowing that the woman intended to commit suicide. As a consequence of the article’s publication, a grand jury was convened to consider a charge of manslaughter against Dr. Quill. Students read the case and then, as part of a classroom-simulated trial, discuss physician-assisted suicide in terms of fundamental medical ethics principles.

Research ethics

Courses in experimental design are frequently part of psychology curricula. They seldom are part of the typical undergraduate programs in other STEM fields, although there is an excellent resource in the text Research Ethics ( 9 ). Apparently, students in STEM disciplines are supposed to absorb the proper canons of behavior by observation and osmosis.

“A Rush to Judgment” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=250&id=250 ) deals with a typical psychological experiment, where a faculty professor is inattentive to his student assistants, one of whom is misrepresenting the results of an experiment. Another student is confronted with a moral dilemma of whether to report this infraction at a potential cost to herself. Involved in the case is a consideration of proper research protocol when dealing with human participants: informed consent, freedom from harm, freedom from coercion, anonymity, and confidentiality. Students are referred to the American Psychological Association's Ethical Principles of Psychologists and Code of Conduct.

“How a Cancer Trial Ended in Betrayal” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=233&id=233 ) begins with a quote from a news item.

Birmingham, Alabama —After Bob Lange spent 8 weeks rubbing an experimental cream, BCX-34, from a prominent biotech company BioCryst on the fiery patches on his body, researchers at the University of Alabama at Birmingham told him the drug was defeating the killer inside him. He felt grateful. “I believed it,” he recalls. “I actually thought I might be cured.” But it was a lie. The drug had no effect on Lange’s rare and potentially fatal skin cancer. And the two key people testing the drug knew it. Lange and 21 other patients were victims of fraud—a scheme made possible by the close tie between the university and the state’s most prominent biotech company. — The Baltimore Sun , June 24, 2001

The authors of this fascinating case state that the learning objectives are to learn the basics of scientific research in a clinical trial; to learn the principles of the scientific method; and to consider the ethical issues involved in clinical trials. Ethical potholes litter the road when universities travel with businesses, and millions of dollars and fame are at stake.

Socio-environmental ethics

Conflicting concerns are the norm when dealing with the environmental problems that beset our world. They not only involve scientific principles, but invariably policy and hurly burly politics as well.

“One Glass for Two People: A Case of Water Use Rights in the Eastern United States” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=603&id=603 ) focuses on the growing issue of water use. Approximately 1.3 million people in North and South Carolina depend on the Catawba-Wateree River for water and electricity. The river is also important for recreation and real estate development. To meet growing water demands, elected officials in Concord and Kannapolis, NC, petitioned their state government to approve an inter-basin transfer of 25 million gallons of water a day from the Catawba River. Other towns in North Carolina and South Carolina that are part of the Catawba-Wateree watershed fought this request for water transfer. For this exercise, students are divided into teams that take the role of different stakeholders trying to negotiate a settlement to this lawsuit. In the course of the debate, students address fundamental legal, ethical, and environmental questions about water use.

“Ecotourism: Who Benefits?” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=359&id=359 ) critically examines the costs and the benefits of visiting fragile, pristine, and relatively undisturbed natural areas. Although ecotourism has among its goals to provide funds for ecological conservation as well as economic benefit and empowerment to local communities, it can result in the exploitation of the natural resources (and communities) it seeks to protect. Students assess ecotourism in Costa Rica by considering the viewpoints of a displaced landowner, banana plantation worker, environmentalist, state official, U.S. trade representative, and national park employee.

Legal ethics

“The Slippery Slope of Litigating Geologic Hazards” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=385&id=385 ) is based on a lawsuit brought against the County of Los Angeles by homeowners suing over damage to their homes in the wake of the Portuguese Bend Landslide. It teaches students principles of landslide movement while illustrating the difficulties involved with litigation resulting from natural hazards. Students first read a newspaper article based on the actual events and then receive details about the geologic setting and landslide characteristics. They are then asked to evaluate the possible causes of the disaster and the responsibilities involved.

“The Sad But True Case of Earl Washington: DNA Analysis and the Criminal Justice System” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=725&id=725 ) recounts how, in 1983, Earl Washington “confessed” to a violent crime that he did not commit and was sentenced to death row. After spending 17 years in prison for something he did not do, Earl was released in 2001 after his innocence was proven through the use of modern DNA technology. The case guides students through the wrongful incarceration of Earl and explores the biological mechanisms behind DNA profiling and the ethical issues involved.

“Complexity in Conservation: The Legal and Ethical Case of a Bird-Eating Cat and its Human Killer” ( http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=664&id=664 ) presents the true story of a Texas man who killed a cat that was killing piping plovers, a type of endangered bird species, and was prosecuted for it. In Texas, it is a crime to kill an animal that “belongs to another,” and there was evidence that another person was feeding the cat, which otherwise appeared to be feral. Students engage in a role-playing activity as jurors; they discuss the case and collectively decide whether the cat killer should be acquitted or convicted. This role-playing coupled with follow-up discussions helps students examine and articulate their own views on a controversial environmental issue and gain a better understanding about the complex interdisciplinary nature of conservation science and practice.

There are plenty of ethical issues in every science classroom to discuss; they are not in short supply. They are hovering around every scientific study that reaches the public eye. Pick any news item with science as its theme and there will be the central question that is often not spoken: should we be doing this research at all, not only because of its economic cost, but because of the social, environmental, or health costs? Surely this should be always a pivotal question in the minds of all citizens. It is sometimes asserted that scientific discovery cannot or should not be stopped—that all knowledge is good. But even if we accept that premise, it seems worthwhile to consider the consequences of our actions. Where else to start than in our classrooms?

Acknowledgments

This material is based upon work supported by the National Science Foundation (NSF) under Grant Nos. DUE-0341279, DUE-0618570, DUE-0920264, and DUE-1323355. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF. The author declares that there are no conflicts of interest.

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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

FOR EMPLOYERS

Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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Case Studies: Bringing Learning to Life and Making Knowledge Stick

Learning by doing is a highly effective and proven strategy for knowledge retention. But sometimes, learning about others who have “done”—using case studies, for example—can be an excellent addition to or replacement for hands-on learning. Case studies―a vital tool in the problem-based learning toolkit—can turbocharge lessons in any subject, but they are particularly useful teaching aids in subjects like Medicine, Law or Forensic Science , where hands-on experiences may not initially be possible.

Here’s a look at how this type of problem-based learning functions to make learning stick and how any faculty member can use them to facilitate deeper, richer learning experiences:

Case studies complement theoretical information 

Reading about scientific principles in a textbook challenges students to think deductively and use their imagination to apply what they’re learning to real-world scenarios. It’s an important skill set. Not all information can or should be packaged up and handed to students, pre-formed; we want students to become critical thinkers and smart decision-makers who are capable of forming their own insights and opinions. 

However, the strategic use of case studies, as a companion to required reading, can help students see theoretical information in a new light, and often for the first time. In short, a case study can bring to life what is often dry and difficult material, transforming it into something powerful, and inspiring students to keep learning. Furthermore, the ability to select or create case studies can give students greater agency in their learning experiences, helping them steer their educational experiences towards topics they find interesting and meaningful. 

What does the research show about using case studies in educational settings? For one, when used in group settings, the use of case studies is proven to promote collaboration while promoting the application of theory. Furthermore, case studies are proven to promote the consideration of diverse cultures, perspectives, and ideas. Beyond that? They help students to broaden their professional acumen —a vitally necessary part of the higher education experience. 

Case studies can be what you want them to be, but they should follow a formula  

Faculty may choose to use case studies in any number of ways, including asking students to read existing case studies, or even challenging them to build their own case studies based on real or hypothetical situations. This can be done individually or in a group. It may be done in the classroom, at home, or in a professional setting. Case studies can take on a wide variety of formats. They may be just a few paragraphs or 30 pages long. They may be prescriptive and challenge readers to create a takeaway or propose a different way of doing things. Or, they may simply ask readers to understand how things were done in a specific case. Beyond written case studies, videos or slide decks can be equally compelling formats. One faculty member even asks students to get theatrical and act out a solution in their sociology class.  

Regardless of format, a case study works best when it roughly follows an arc of problem, solution and results. All case studies must present a problem that doesn’t have an immediately clear solution or result. For example, a medical student may read a case study detailing the hospital admission of a 42-year-old woman who presents to the emergency room with persistent and severe calf pain, but has normal blood tests and ultrasound imaging. What should the physician consider next? A law student might read a case study about an elderly man involved in a car accident who denies any memory of the event. What legal angles should be considered?

Case studies – get started

Are you eager to use case studies with your students? Cengage higher education titles typically contain case studies and real-world examples that bring learning to life and help knowledge stick. Below are some learning materials, spanning a range of subjects, that can help your students reap the proven benefits of case study learning:

Accounting, 29e

Award-winning authors Carl Warren, Jefferson P. Jones and William B. Tayler offer students the opportunity to analyze real-world business decisions and show how accounting is used by real companies.

Guide to Computer Forensics and Investigations, 7e

“Guide to Computer Forensics and Investigations” by Bill Nelson, Amelia Phillips, Christopher Steuart and Robert S. Wilson includes case projects aimed at providing practical implementation experience, as well as practice in applying critical thinking skills.

Business Ethics: Case Studies and Selected Readings, 10e

Marianne M. Jennings’ best-selling “Business Ethics: Case Studies and Selected Readings, 10e” explores a proven process for analyzing ethical dilemmas and creating stronger values.

Anatomy & Physiology, 1e

Author Dr. Liz Co includes a chapter composed entirely of case studies to give students additional practice in critical thinking. The cases can be assigned at the end of the semester or at intervals as the instructor chooses.

Psychopathology and Life: A Dimensional Approach, 11e

Christopher Kearney offers a concise, contemporary and science-based view of psychopathology that emphasizes the individual first. Geared toward cases to which most college students can relate, helping them understand that symptoms of psychological problems occur in many people in different ways.

Understanding Psychological Disorders Enhanced, 12e

In “Understanding Psychological Disorders Enhanced” by David Sue, Derald Sue, Diane M. Sue and Stanley Sue, students can explore current events, real-world case studies and the latest developments from the field.

Policing in the US: Past, Present, and Future, 1e

This comprehensive and timely text by Lorenzo M. Boyd, Melissa S. Morabito and Larry J. Siegel examines the current state of American policing, offering a fresh and balanced look at contemporary issues in law enforcement. Each chapter opens with a real-life case or incident.

Public Speaking: The Evolving Art, 5e

With a student-centered approach, “Public Speaking: The Evolving Art” by Stephanie J. Coopman and James Lull includes innovative solutions to current issues, including critically assessing the credibility of information sources. A diverse collection of sample student and professional presentations encourage students to consider chapter concepts in the context of real speeches.

Theory and Practice of Counseling and Psychotherapies, 11e

Dr. Gerald Corey’s best-selling text helps readers compare and contrast the therapeutic models expressed in counseling theories.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Table of Contents

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Study provides blueprint for hybrid-virtual home visit model to support patients who do not live close to a hospital

I n a new study, a team developed and successfully tested a hybrid-virtual home visit model that provides care to veterans who do not live close to a VA health care facility. The work is published in the Journal of General Internal Medicine .

U.S. Department of Veteran Affairs (VA), Regenstrief Institute, and Indiana University School of Medicine research scientists Dawn Bravata, M.D., and Teresa Damush, Ph.D., helped lead the team.

The results from the study demonstrate the feasibility of implementing a hybrid-virtual home visit model to care for high-risk, community-dwelling older persons. Two clinical cases illustrated how this model cared for patients who might not otherwise have received timely health care.

The researchers suggest a widespread deployment of hybrid-virtual home visit model programs will be required to support the veteran population as they age in place.

"We were able to successfully convert an in-person home visit, conducted by nurse practitioners and social workers, to a hybrid-virtual model where we had a telehealth technician in the patient's home working virtually with the nurse practitioner and social worker to provide care," said Dr. Bravata, a co-principal investigator and senior author of the study.

"Having the telehealth technician drive to patients' homes allowed the nurse practitioners and social workers to telework, which gave them more time to provide patient care."

The hybrid-virtual model, known as TeleGRACE, is an extension of the established Geriatric Resources for Assessment and Care of Elders (VA-GRACE) program. VA-GRACE is a multidisciplinary care model which provides comprehensive home-based geriatric evaluation and management for older veterans residing within a 20-mile drive radius from the Indianapolis VA facility.

TeleGRACE expands access to VA-GRACE services by enrolling patients living within a 60-mile radius. TeleGRACE provides all of the same services as VA-GRACE, except it's a hybrid-virtual home visit instead of the in-person home visit. The VA has been seeking to expand access to evidence-based practices supporting community-dwelling older persons like the VA-GRACE program.

Case examples

The first case examination followed a patient scheduled for a TeleGRACE enrollment visit after being discharged from a VA in-patient admission. Before the visit, the patient sought care for a leg wound in a non-VA emergency department closer to home. Working remotely, the nurse practitioner identified that the patient needed additional follow-up care for the wound.

The nurse practitioner used pictures of the wound taken by the telehealth technician and sent them to the VA wound care service. The wound care team reviewed the pictures, determined the appropriate care, collaborated with the VA-GRACE social worker to order home-health wound care, and sent wound care supplies to the patient's home, all during the single TeleGRACE visit.

"The patient would typically have had to go through a couple of clinic visits to receive the right care if it weren't for the TeleGRACE visit. The telehealth technician provided the patient with the wound care they needed in one visit," said Dr. Bravata.

In the second case, during a TeleGRACE enrollment visit, a patient who had been discharged from a VA inpatient stay 13 days prior became unwell. The telehealth technician obtained vital signs with the nurse practitioner participating remotely.

The patient was then taken to the emergency department and admitted to the hospital for a 5-day stay. The patient told the emergency department staff and inpatient teams that the TeleGRACE program saved his life.

The researchers described the challenges encountered during the pre-implementation phase and the solutions they developed during program development.

"Previous studies have identified that geriatric patients have difficulty connecting with virtual health care. The TeleGRACE program overcomes many of these issues," said Dr. Bravata.

"For example, consider patients with visual or hearing impairment or perhaps mild cognitive impairment—it's helpful to have the telehealth technician physically in the homes troubleshooting equipment and providing assistance."

To implement the hybrid-virtual care model, five program domains required attention and problem-solving:

• Telehealth connectivity and equipment
• Virtual physical examination
• Protocols and procedures
• Staff training
• Team integration

More information: Cathy C. Schubert et al, Expanding Access to Comprehensive Geriatric Evaluation via Telehealth: Development of Hybrid-Virtual Home Visits, Journal of General Internal Medicine (2024). DOI: 10.1007/s11606-023-08460-5

Provided by Regenstrief Institute

Circular economy introduction

What is a circular economy?

Other available languages

• Circular economy explained

The circular economy is a system where materials never become waste and nature is regenerated. In a circular economy, products and materials are kept in circulation through processes like maintenance, reuse, refurbishment, remanufacture, recycling, and composting. The circular economy tackles climate change and other global challenges, like biodiversity loss, waste, and pollution, by decoupling economic activity from the consumption of finite resources.

The circular economy is based on three principles, driven by design:

Eliminate waste and pollution.

Circulate products and materials (at their highest value)

Regenerate nature

In our current economy, we take materials from the Earth, make products from them, and eventually throw them away as waste – the process is linear. In a circular economy, by contrast, we stop waste being produced in the first place.

We must transform every element of our take-make-waste system: how we manage resources, how we make and use products, and what we do with the materials afterwards. Only then can we create a thriving circular economy that can benefit everyone within the limits of our planet.

A way to transform our system

What will it take to transform our throwaway economy into one where waste is eliminated, resources are circulated, and nature is regenerated?

The circular economy gives us the tools to tackle climate change and biodiversity loss together, while addressing important social needs.

It gives us the power to grow prosperity, jobs, and resilience while cutting greenhouse gas emissions, waste, and pollution.

How the circular economy works

Get the basics or explore the circular economy in detail.

The butterfly diagram: visualising the circular economy

The 'butterfly diagram' shows the continuous flow of materials in a circular economy.

Explaining the circular economy: re-thinking progress

Watch our beginner's guide to understanding how a circular economy works.

Circular economy key ideas

Get up to speed with the fundamentals of the circular economy with these content pills or dive deep...

Circular economy principles

A circular economy is based on three principles, all driven by design.

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Circulate products and materials

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Natura Brazil

Working with nature to make food last longer

Apeel is a company that has come up with an innovative way to eliminate single-use shrink wrap plastic packaging on fresh fruit and veg, while at the same time tackling food waste.

Apeel is a layer of edible, plant-based coating applied to fresh products that mimics and enhances the natural defences of fruit and vegetables. This slows down the two main things that cause spoilage – water loss and oxidation.

Case studies

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Ellen MacArthur Foundation FILMS

In this series of circular economy events, experts discuss how circular economy solutions are...

Podcast: What is the circular economy?

Ellen MacArthur discusses a range of topics in a conversation about the circular economy.

Circular Economy: The Big Idea

Delve into the concepts of the circular economy to understand its origin, design, and importance...

Circular economy courses

If you want to deepen your knowledge of the circular economy, learn how to stay ahead of the game...

Video series: What is the circular economy?

Building a Regenerative, Restorative Economy

An Introduction to the Ellen MacArthur Foundation

Basics of a circular economy

Our team discuss what the circular economy means to them.

Discussing Circular Economy and What It Means

In this live conversation, members of the Ellen MacArthur Foundation team will discuss and debate...

What If We Don’t Buy Products and We Buy Service? Circular Economy Explained

With the price of resources and energy becoming increasingly volatile, can today's linear economy...

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Biodiversity and the circular economy

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Built environment and the circular economy

In a circular economy our built environment can be a force for good

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Artificial intelligence  is being used in healthcare for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals.

According to  Statista , the artificial intelligence (AI) healthcare market, which is valued at $11 billion in 2021, is projected to be worth $187 billion in 2030. That massive increase means we will likely continue to see considerable changes in how medical providers, hospitals, pharmaceutical and biotechnology companies, and others in the healthcare industry operate.

Better  machine learning (ML)  algorithms, more access to data, cheaper hardware, and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. AI and ML technologies can sift through enormous volumes of health data—from health records and clinical studies to genetic information—and analyze it much faster than humans.

Healthcare organizations are using AI to improve the efficiency of all kinds of processes, from back-office tasks to patient care. The following are some examples of how AI might be used to benefit staff and patients:

• Administrative workflow:  Healthcare workers spend a lot of time doing paperwork and other administrative tasks. AI and automation can help perform many of those mundane tasks, freeing up employee time for other activities and giving them more face-to-face time with patients. For example, generative AI can help clinicians with note-taking and content summarization that can help keep medical records as thoroughly as possible. AI might also help with accurate coding and sharing of information between departments and billing.
• Virtual nursing assistants:  One study found that  64% of patients  are comfortable with the use of AI for around-the-clock access to answers that support nurses provide. AI virtual nurse assistants—which are AI-powered chatbots, apps, or other interfaces—can be used to help answer questions about medications, forward reports to doctors or surgeons and help patients schedule a visit with a physician. These sorts of routine tasks can help take work off the hands of clinical staff, who can then spend more time directly on patient care, where human judgment and interaction matter most.
• Dosage error reduction:  AI can be used to help identify errors in how a patient self-administers medication. One example comes from a study in  Nature Medicine , which found that up to 70% of patients don’t take insulin as prescribed. An AI-powered tool that sits in the patient’s background (much like a wifi router) might be used to flag errors in how the patient administers an insulin pen or inhaler.
• Less invasive surgeries:  AI-enabled robots might be used to work around sensitive organs and tissues to help reduce blood loss, infection risk and post-surgery pain.
• Fraud prevention:  Fraud in the healthcare industry is enormous, at $380 billion/year, and raises the cost of consumers’ medical premiums and out-of-pocket expenses. Implementing AI can help recognize unusual or suspicious patterns in insurance claims, such as billing for costly services or procedures that are not performed, unbundling (which is billing for the individual steps of a procedure as though they were separate procedures), and performing unnecessary tests to take advantage of insurance payments.

A recent study found that  83% of patients  report poor communication as the worst part of their experience, demonstrating a strong need for clearer communication between patients and providers. AI technologies like  natural language processing  (NLP), predictive analytics, and  speech recognition  might help healthcare providers have more effective communication with patients. AI might, for instance, deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making.

According to  Harvard’s School of Public Health , although it’s early days for this use, using AI to make diagnoses may reduce treatment costs by up to 50% and improve health outcomes by 40%.

One use case example is out of the  University of Hawaii , where a research team found that deploying  deep learning  AI technology can improve breast cancer risk prediction. More research is needed, but the lead researcher pointed out that an AI algorithm can be trained on a much larger set of images than a radiologist—as many as a million or more radiology images. Also, that algorithm can be replicated at no cost except for hardware.

An  MIT group  developed an ML algorithm to determine when a human expert is needed. In some instances, such as identifying cardiomegaly in chest X-rays, they found that a hybrid human-AI model produced the best results.

Another  published study  found that AI recognized skin cancer better than experienced doctors.  US, German and French researchers used deep learning on more than 100,000 images to identify skin cancer. Comparing the results of AI to those of 58 international dermatologists, they found AI did better.

As health and fitness monitors become more popular and more people use apps that track and analyze details about their health. They can share these real-time data sets with their doctors to monitor health issues and provide alerts in case of problems.

AI solutions—such as big data applications, machine learning algorithms and deep learning algorithms—might also be used to help humans analyze large data sets to help clinical and other decision-making. AI might also be used to help detect and track infectious diseases, such as COVID-19, tuberculosis, and malaria.

One benefit the use of AI brings to health systems is making gathering and sharing information easier. AI can help providers keep track of patient data more efficiently.

One example is diabetes. According to the  Centers for Disease Control and Prevention , 10% of the US population has diabetes. Patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team. AI can help providers gather that information, store, and analyze it, and provide data-driven insights from vast numbers of people. Using this information can help healthcare professionals determine how to better treat and manage diseases.

Organizations are also starting to use AI to help improve drug safety. The company SELTA SQUARE, for example, is  innovating the pharmacovigilance (PV) process , a legally mandated discipline for detecting and reporting adverse effects from drugs, then assessing, understanding, and preventing those effects. PV demands significant effort and diligence from pharma producers because it’s performed from the clinical trials phase all the way through the drug’s lifetime availability. Selta Square uses a combination of AI and automation to make the PV process faster and more accurate, which helps make medicines safer for people worldwide.

Sometimes, AI might reduce the need to test potential drug compounds physically, which is an enormous cost-savings.  High-fidelity molecular simulations  can run on computers without incurring the high costs of traditional discovery methods.

AI also has the potential to help humans predict toxicity, bioactivity, and other characteristics of molecules or create previously unknown drug molecules from scratch.

As AI becomes more important in healthcare delivery and more AI medical applications are developed, ethical, and regulatory governance must be established. Issues that raise concern include the possibility of bias, lack of transparency, privacy concerns regarding data used for training AI models, and safety and liability issues.

“AI governance is necessary, especially for clinical applications of the technology,” said Laura Craft, VP Analyst at  Gartner . “However, because new AI techniques are largely new territory for most [health delivery organizations], there is a lack of common rules, processes, and guidelines for eager entrepreneurs to follow as they design their pilots.”

The World Health Organization (WHO) spent 18 months deliberating with leading experts in ethics, digital technology, law, and human rights and various Ministries of Health members to produce a report that is called  Ethics & Governance of Artificial Intelligence for Health . This report identifies ethical challenges to using AI in healthcare, identifies risks, and outlines six  consensus principles  to ensure AI works for the public’s benefit:

• Protecting autonomy
• Promoting human safety and well-being
• Ensuring transparency
• Fostering accountability
• Ensuring equity
• Promoting tools that are responsive and sustainable

The WHO report also provides recommendations that ensure governing AI for healthcare both maximizes the technology’s promise and holds healthcare workers accountable and responsive to the communities and people they work with.

AI provides opportunities to help reduce human error, assist medical professionals and staff, and provide patient services 24/7. As AI tools continue to develop, there is potential to use AI even more in reading medical images, X-rays and scans, diagnosing medical problems and creating treatment plans.

AI applications continue to help streamline various tasks, from answering phones to analyzing population health trends (and likely, applications yet to be considered). For instance, future AI tools may automate or augment more of the work of clinicians and staff members. That will free up humans to spend more time on more effective and compassionate face-to-face professional care.

When patients need help, they don’t want to (or can’t) wait on hold. Healthcare facilities’ resources are finite, so help isn’t always available instantaneously or 24/7—and even slight delays can create frustration and feelings of isolation or cause certain conditions to worsen.

IBM® watsonx Assistant™ AI healthcare chatbots  can help providers do two things: keep their time focused where it needs to be and empower patients who call in to get quick answers to simple questions.

IBM watsonx Assistant  is built on deep learning, machine learning and natural language processing (NLP) models to understand questions, search for the best answers and complete transactions by using conversational AI.

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Top 10 Project Management Case Studies with Examples 2024

1. nasa's mars exploration rover: innovative project management in space exploration., 2. apple's iphone development: delivering revolutionary products with precision., 3. tesla's gigafactory construction: exemplary project execution in renewable energy., 4. netflix's content expansion: agile management in the entertainment industry., 5. amazon's prime air drone delivery: pioneering logistics project management., 6. google's waymo self-driving cars: cutting-edge technology meets project efficiency., 7. mcdonald's digital transformation: adaptive project management in fast food., 8. ikea's sustainable store design: eco-friendly project implementation in retail., 9. unicef's vaccine distribution: humanitarian project management at scale., 10. spacex's starlink satellite network: revolutionizing global connectivity with project prowess., discover more stories.