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37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Adventures in financial data science, gecoagent: a conversational agent for empowering genomic data extraction and analysis.

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.

Differentially Private Medical Texts Generation Using Generative Neural Networks

Technological advancements in data science have offered us affordable storage and efficient algorithms to query a large volume of data. Our health records are a significant part of this data, which is pivotal for healthcare providers and can be utilized in our well-being. The clinical note in electronic health records is one such category that collects a patient’s complete medical information during different timesteps of patient care available in the form of free-texts. Thus, these unstructured textual notes contain events from a patient’s admission to discharge, which can prove to be significant for future medical decisions. However, since these texts also contain sensitive information about the patient and the attending medical professionals, such notes cannot be shared publicly. This privacy issue has thwarted timely discoveries on this plethora of untapped information. Therefore, in this work, we intend to generate synthetic medical texts from a private or sanitized (de-identified) clinical text corpus and analyze their utility rigorously in different metrics and levels. Experimental results promote the applicability of our generated data as it achieves more than 80\% accuracy in different pragmatic classification problems and matches (or outperforms) the original text data.

Impact on Stock Market across Covid-19 Outbreak

Abstract: This paper analysis the impact of pandemic over the global stock exchange. The stock listing values are determined by variety of factors including the seasonal changes, catastrophic calamities, pandemic, fiscal year change and many more. This paper significantly provides analysis on the variation of listing price over the world-wide outbreak of novel corona virus. The key reason to imply upon this outbreak was to provide notion on underlying regulation of stock exchanges. Daily closing prices of the stock indices from January 2017 to January 2022 has been utilized for the analysis. The predominant feature of the research is to analyse the fact that does global economy downfall impacts the financial stock exchange. Keywords: Stock Exchange, Matplotlib, Streamlit, Data Science, Web scrapping.

Information Resilience: the nexus of responsible and agile approaches to information use

AbstractThe appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the diverse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.

qEEG Analysis in the Diagnosis of Alzheimers Disease; a Comparison of Functional Connectivity and Spectral Analysis

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

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99+ Interesting Data Science Research Topics For Students In 2024

In today’s information-driven world, data science research stands as a pivotal domain shaping our understanding and application of vast data sets. It amalgamates statistics, computer science, and domain knowledge to extract valuable insights from data. Understanding ‘What Is Data Science?’ is fundamental—a field exploring patterns, trends, and solutions embedded within data.

However, the significance of data science research papers in a student’s life cannot be overstated. They foster critical thinking, analytical skills, and a deeper comprehension of the subject matter. To aid students in navigating this realm effectively, this blog dives into essential elements integral to a data science research paper, while also offering a goldmine of 99+ engaging and timely data science research topics for 2024.

Unraveling tips for crafting an impactful research paper and insights on choosing the right topic, this blog is a compass for students exploring data science research topics. Stay tuned to unearth more about ‘data science research topics’ and refine your academic journey.

What Is Data Science?

Table of Contents

Data Science is like a detective for information! It’s all about uncovering secrets and finding valuable stuff in heaps of data. Imagine you have a giant puzzle with tons of pieces scattered around. Data Science helps in sorting these pieces and figuring out the picture they create. It uses tools and skills from math, computer science, and knowledge about different fields to solve real-world problems.

In simpler terms, Data Science is like a chef in a kitchen, blending ingredients to create a perfect dish. Instead of food, it combines data—numbers, words, pictures—to cook up solutions. It helps in understanding patterns, making predictions, and answering tricky questions by exploring data from various sources. In essence, Data Science is the magic that turns data chaos into meaningful insights that can guide decisions and make life better.

Importance Of Data Science Research Paper In Student’s Life

Data Science research papers are like treasure maps for students! They’re super important because they teach students how to explore and understand the world of data. Writing these papers helps students develop problem-solving skills, think critically, and become better at analyzing information. It’s like a fun adventure where they learn how to dig into data and uncover valuable insights that can solve real-world problems.

• Enhances critical thinking: Research papers challenge students to analyze and interpret data critically, honing their thinking skills.
• Fosters analytical abilities: Students learn to sift through vast amounts of data, extracting meaningful patterns and information.
• Encourages exploration: Engaging in research encourages students to explore diverse data sources, broadening their knowledge horizon.
• Develops communication skills: Writing research papers hones students’ ability to articulate complex findings and ideas clearly.
• Prepares for real-world challenges: Through research, students learn to apply theoretical knowledge to practical problems, preparing them for future endeavors.

Elements That Must Be Present In Data Science Research Paper

Here are some elements that must be present in data science research paper:

1. Clear Objective

A data science research paper should start with a clear goal, stating what the study aims to investigate or achieve. This objective guides the entire paper, helping readers understand the purpose and direction of the research.

2. Detailed Methodology

Explaining how the research was conducted is crucial. The paper should outline the tools, techniques, and steps used to collect, analyze, and interpret data. This section allows others to replicate the study and validate its findings.

3. Accurate Data Presentation

Presenting data in an organized and understandable manner is key. Graphs, charts, and tables should be used to illustrate findings clearly, aiding readers’ comprehension of the results.

4. Thorough Analysis and Interpretation

Simply presenting data isn’t enough; the paper should delve into a deep analysis, explaining the meaning behind the numbers. Interpretation helps draw conclusions and insights from the data.

5. Conclusive Findings and Recommendations

A strong conclusion summarizes the key findings of the research. It should also offer suggestions or recommendations based on the study’s outcomes, indicating potential avenues for future exploration.

Here are some interesting data science research topics for students in 2024:

Natural Language Processing (NLP)

• Multi-modal Contextual Understanding: Integrating text, images, and audio to enhance NLP models’ comprehension abilities.
• Cross-lingual Transfer Learning: Investigating methods to transfer knowledge from one language to another for improved translation and understanding.
• Emotion Detection in Text: Developing models to accurately detect and interpret emotions conveyed in textual content.
• Sarcasm Detection in Social Media: Building algorithms that can identify and understand sarcastic remarks in online conversations.
• Language Generation for Code: Generating code snippets and scripts from natural language descriptions using NLP techniques.
• Bias Mitigation in Language Models: Developing strategies to mitigate biases present in large language models and ensure fairness in generated content.
• Dialogue Systems for Personalized Assistance: Creating intelligent conversational agents that provide personalized assistance based on user preferences and history.
• Summarization of Legal Documents: Developing NLP models capable of summarizing lengthy legal documents for quick understanding and analysis.
• Understanding Contextual Nuances in Sentiment Analysis: Enhancing sentiment analysis models to better comprehend contextual nuances and sarcasm in text.
• Hate Speech Detection and Moderation: Building systems to detect and moderate hate speech and offensive language in online content.

Computer Vision

• Weakly Supervised Object Detection: Exploring methods to train object detection models with limited annotated data.
• Video Action Recognition in Uncontrolled Environments: Developing models that can recognize human actions in videos captured in uncontrolled settings.
• Image Generation and Translation: Investigating techniques to generate realistic images from textual descriptions and translate images across different domains.
• Scene Understanding in Autonomous Systems: Enhancing computer vision algorithms for better scene understanding in autonomous vehicles and robotics.
• Fine-grained Visual Classification: Improving models to classify objects at a more granular level, distinguishing subtle differences within similar categories.
• Visual Question Answering (VQA): Creating systems capable of answering questions based on visual input, requiring reasoning and comprehension abilities.
• Medical Image Analysis for Disease Diagnosis: Developing computer vision models for accurate and early diagnosis of diseases from medical images.
• Action Localization in Videos: Building models to precisely localize and recognize specific actions within video sequences.
• Image Captioning with Contextual Understanding: Generating captions for images considering the context and relationships between objects.
• Human Pose Estimation in Real-time: Improving algorithms for real-time estimation of human poses in videos for applications like motion analysis and gaming.

Machine Learning Algorithms

• Self-supervised Learning Techniques: Exploring novel methods for training machine learning models without explicit supervision.
• Continual Learning in Dynamic Environments: Investigating algorithms that can continuously learn and adapt to changing data distributions and tasks.
• Explainable AI for Model Interpretability: Developing techniques to explain the decisions and predictions made by complex machine learning models.
• Transfer Learning for Small Datasets: Techniques to effectively transfer knowledge from large datasets to small or domain-specific datasets.
• Adaptive Learning Rate Optimization: Enhancing optimization algorithms to dynamically adjust learning rates based on data characteristics.
• Robustness to Adversarial Attacks: Building models resistant to adversarial attacks, ensuring stability and security in machine learning applications.
• Active Learning Strategies: Investigating methods to select and label the most informative data points for model training to minimize labeling efforts.
• Privacy-preserving Machine Learning: Developing algorithms that can train models on sensitive data while preserving individual privacy.
• Fairness-aware Machine Learning: Techniques to ensure fairness and mitigate biases in machine learning models across different demographics.
• Multi-task Learning for Jointly Learning Tasks: Exploring approaches to jointly train models on multiple related tasks to improve overall performance.

Deep Learning

• Graph Neural Networks for Representation Learning: Using deep learning techniques to learn representations from graph-structured data.
• Transformer Models for Image Processing: Adapting transformer architectures for image-related tasks, such as image classification and generation.
• Few-shot Learning Strategies: Investigating methods to enable deep learning models to learn from a few examples in new categories.
• Memory-Augmented Neural Networks: Enhancing neural networks with external memory for improved learning and reasoning capabilities.
• Neural Architecture Search (NAS): Automating the design of neural network architectures for specific tasks or constraints.
• Meta-learning for Fast Adaptation: Developing models capable of quickly adapting to new tasks or domains with minimal data.
• Deep Reinforcement Learning for Robotics: Utilizing deep RL techniques for training robots to perform complex tasks in real-world environments.
• Generative Adversarial Networks (GANs) for Data Augmentation: Using GANs to generate synthetic data for enhancing training datasets.
• Variational Autoencoders for Unsupervised Learning: Exploring VAEs for learning latent representations of data without explicit supervision.
• Lifelong Learning in Deep Networks: Strategies to enable deep networks to continually learn from new data while retaining past knowledge.

Big Data Analytics

• Streaming Data Analysis for Real-time Insights: Techniques to analyze and derive insights from continuous streams of data in real-time.
• Scalable Algorithms for Massive Graphs: Developing algorithms that can efficiently process and analyze large-scale graph-structured data.
• Anomaly Detection in High-dimensional Data: Detecting anomalies and outliers in high-dimensional datasets using advanced statistical methods and machine learning.
• Personalization and Recommendation Systems: Enhancing recommendation algorithms for providing personalized and relevant suggestions to users.
• Data Quality Assessment and Improvement: Methods to assess, clean, and enhance the quality of big data to improve analysis and decision-making.
• Time-to-Event Prediction in Time-series Data: Predicting future events or occurrences based on historical time-series data.
• Geospatial Data Analysis and Visualization: Analyzing and visualizing large-scale geospatial data for various applications such as urban planning, disaster management, etc.
• Privacy-preserving Big Data Analytics: Ensuring data privacy while performing analytics on large-scale datasets in distributed environments.
• Graph-based Deep Learning for Network Analysis: Leveraging deep learning techniques for network analysis and community detection in large-scale networks.
• Dynamic Data Compression Techniques: Developing methods to compress and store large volumes of data efficiently without losing critical information.

Healthcare Analytics

• Predictive Modeling for Patient Outcomes: Using machine learning to predict patient outcomes and personalize treatments based on individual health data.
• Clinical Natural Language Processing for Electronic Health Records (EHR): Extracting valuable information from unstructured EHR data to improve healthcare delivery.
• Wearable Devices and Health Monitoring: Analyzing data from wearable devices to monitor and predict health conditions in real-time.
• Drug Discovery and Development using AI: Utilizing machine learning and AI for efficient drug discovery and development processes.
• Predictive Maintenance in Healthcare Equipment: Developing models to predict and prevent equipment failures in healthcare settings.
• Disease Clustering and Stratification: Grouping diseases based on similarities in symptoms, genetic markers, and response to treatments.
• Telemedicine Analytics: Analyzing data from telemedicine platforms to improve remote healthcare delivery and patient outcomes.
• AI-driven Radiomics for Medical Imaging: Using AI to extract quantitative features from medical images for improved diagnosis and treatment planning.
• Healthcare Resource Optimization: Optimizing resource allocation in healthcare facilities using predictive analytics and operational research techniques.
• Patient Journey Analysis and Personalized Care Pathways: Analyzing patient trajectories to create personalized care pathways and improve healthcare outcomes.

Time Series Analysis

• Forecasting Volatility in Financial Markets: Predicting and modeling volatility in stock prices and financial markets using time series analysis.
• Dynamic Time Warping for Similarity Analysis: Utilizing DTW to measure similarities between time series data, especially in scenarios with temporal distortions.
• Seasonal Pattern Detection and Analysis: Identifying and modeling seasonal patterns in time series data for better forecasting.
• Time Series Anomaly Detection in Industrial IoT: Detecting anomalies in industrial sensor data streams to prevent equipment failures and improve maintenance.
• Multivariate Time Series Forecasting: Developing models to forecast multiple related time series simultaneously, considering interdependencies.
• Non-linear Time Series Analysis Techniques: Exploring non-linear models and methods for analyzing complex time series data.
• Time Series Data Compression for Efficient Storage: Techniques to compress and store time series data efficiently without losing crucial information.
• Event Detection and Classification in Time Series: Identifying and categorizing specific events or patterns within time series data.
• Time Series Forecasting with Uncertainty Estimation: Incorporating uncertainty estimation into time series forecasting models for better decision-making.
• Dynamic Time Series Graphs for Network Analysis: Representing and analyzing dynamic relationships between entities over time using time series graphs.

Reinforcement Learning

• Multi-agent Reinforcement Learning for Collaboration: Developing strategies for multiple agents to collaborate and solve complex tasks together.
• Hierarchical Reinforcement Learning: Utilizing hierarchical structures in RL for solving tasks with varying levels of abstraction and complexity.
• Model-based Reinforcement Learning for Sample Efficiency: Incorporating learned models into RL for efficient exploration and planning.
• Robotic Manipulation with Reinforcement Learning: Training robots to perform dexterous manipulation tasks using RL algorithms.
• Safe Reinforcement Learning: Ensuring that RL agents operate safely and ethically in real-world environments, minimizing risks.
• Transfer Learning in Reinforcement Learning: Transferring knowledge from previously learned tasks to expedite learning in new but related tasks.
• Curriculum Learning Strategies in RL: Designing learning curricula to gradually expose RL agents to increasingly complex tasks.
• Continuous Control in Reinforcement Learning: Exploring techniques for continuous control tasks that require precise actions in a continuous action space.
• Reinforcement Learning for Adaptive Personalization: Utilizing RL to personalize experiences or recommendations for individuals in dynamic environments.
• Reinforcement Learning in Healthcare Decision-making: Using RL to optimize treatment strategies and decision-making in healthcare settings.

Data Mining

• Graph Mining for Social Network Analysis: Extracting valuable insights from social network data using graph mining techniques.
• Sequential Pattern Mining for Market Basket Analysis: Discovering sequential patterns in customer purchase behaviors for market basket analysis.
• Clustering Algorithms for High-dimensional Data: Developing clustering techniques suitable for high-dimensional datasets.
• Frequent Pattern Mining in Healthcare Datasets: Identifying frequent patterns in healthcare data for actionable insights and decision support.
• Outlier Detection and Fraud Analysis: Detecting anomalies and fraudulent activities in various domains using data mining approaches.
• Opinion Mining and Sentiment Analysis in Reviews: Analyzing opinions and sentiments expressed in product or service reviews to derive insights.
• Data Mining for Personalized Learning: Mining educational data to personalize learning experiences and adapt teaching methods.
• Association Rule Mining in Internet of Things (IoT) Data: Discovering meaningful associations and patterns in IoT-generated data streams.
• Multi-modal Data Fusion for Comprehensive Analysis: Integrating information from multiple data modalities for a holistic understanding and analysis.
• Data Mining for Energy Consumption Patterns: Analyzing energy usage data to identify patterns and optimize energy consumption in various sectors.

Ethical AI and Bias Mitigation

• Fairness Metrics and Evaluation in AI Systems: Developing metrics and evaluation frameworks to assess the fairness of AI models.
• Bias Detection and Mitigation in Facial Recognition: Addressing biases present in facial recognition systems to ensure equitable performance across demographics.
• Algorithmic Transparency and Explainability: Designing methods to make AI algorithms more transparent and understandable to stakeholders.
• Fair Representation Learning in Unbalanced Datasets: Learning fair representations from imbalanced data to reduce biases in downstream tasks.
• Fairness-aware Recommender Systems: Ensuring fairness and reducing biases in recommendation algorithms across diverse user groups.
• Ethical Considerations in AI for Criminal Justice: Investigating the ethical implications of AI-based decision-making in criminal justice systems.
• Debiasing Techniques in Natural Language Processing: Developing methods to mitigate biases in language models and text generation.
• Diversity and Fairness in Hiring Algorithms: Ensuring diversity and fairness in AI-based hiring systems to minimize biases in candidate selection.
• Ethical AI Governance and Policy: Examining the role of governance and policy frameworks in regulating the development and deployment of AI systems.
• AI Accountability and Responsibility: Addressing ethical dilemmas and defining responsibilities concerning AI system behaviors and decision-making processes.

Tips For Writing An Effective Data Science Research Paper

Here are some tips for writing an effective data science research paper:

Tip 1: Thorough Planning and Organization

Begin by planning your research paper carefully. Outline the sections and information you’ll include, ensuring a logical flow from introduction to conclusion. This organized approach makes writing easier and helps maintain coherence in your paper.

Tip 2: Clarity in Writing Style

Use clear and simple language to communicate your ideas. Avoid jargon or complex terms that might confuse readers. Write in a way that is easy to understand, ensuring your message is effectively conveyed.

Tip 3: Precise and Relevant Information

Include only information directly related to your research topic. Ensure the data, explanations, and examples you use are precise and contribute directly to supporting your arguments or findings.

Tip 4: Effective Data Visualization

Utilize graphs, charts, and tables to present your data visually. Visual aids make complex information easier to comprehend and can enhance the overall presentation of your research findings.

Tip 5: Review and Revise

Before submitting your paper, review it thoroughly. Check for any errors in grammar, spelling, or formatting. Revise sections if necessary to ensure clarity and coherence in your writing. Asking someone else to review it can also provide valuable feedback.

• Hospitality Management Research Topics

Things To Remember While Choosing The Data Science Research Topic

When selecting a data science research topic, consider your interests and its relevance to the field. Ensure the topic is neither too broad nor too narrow, striking a balance that allows for in-depth exploration while staying manageable.

• Relevance and Significance: Choose a topic that aligns with current trends or addresses a significant issue in the field of data science.
• Feasibility : Ensure the topic is researchable within the resources and time available. It should be practical and manageable for the scope of your study.
• Your Interest and Passion: Select a topic that genuinely interests you. Your enthusiasm will drive your motivation and engagement throughout the research process.
• Availability of Data: Check if there’s sufficient data available for analysis related to your chosen topic. Accessible and reliable data sources are vital for thorough research.
• Potential Contribution: Consider how your chosen topic can contribute to existing knowledge or fill a gap in the field. Aim for a topic that adds value and insights to the data science domain.

In wrapping up our exploration of data science research topics, we’ve uncovered a world of importance and guidance for students. From defining data science to understanding its impact on student life, identifying essential elements in research papers, offering a multitude of intriguing topics for 2024, to providing tips for crafting effective papers—the journey has been insightful. 

Remembering the significance of topic selection and the key components of a well-structured paper, this voyage emphasizes how data science opens doors to endless opportunities. It’s not just a subject; it’s the compass guiding tomorrow’s discoveries and innovations in our digital landscape.

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Data Science in Healthcare: COVID-19 and Beyond

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available ‘big’ data [ 1 ]. The recent advances in data science and AI have had a major impact on healthcare already, as can be seen in the recent biomedical literature [ 2 ]. Improved sharing and analysis of medical data results in earlier and better diagnoses, and more patient-tailored treatments. This increased data sharing, in combination with advances in health data management, works hand-in-hand with trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated healthcare delivery. Using data science and AI, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population level [ 3 ]. AI can be applied in all three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation [ 4 ]. ML algorithms can make predictions on how a disease will develop or respond to treatment, deep learning algorithms can find malignant tumors in magnetic resonance (MR) images and digital pathology images, and natural language-processing (NLP) algorithms can analyze unstructured documents with high speed and accuracy. These are just a few examples of what data science can do. This Special Issue focuses on how data science and AI are used in healthcare, and on related topics such as data sharing and data management. Since this Special Issue contains papers from 2020 to 2022, naturally there are a few papers about the COVID-19 pandemic: one on the determination of potential risk factors for the case fatality rate, one on the analysis of Arabic Twitter data to detect government pandemic measures and public concerns, and one on an enhanced sentinel surveillance system for outbreak prediction. There are also papers about data-sharing initiatives, depression treatment, the relationship between depression and metabolic status, cardiac thoracic pain, hand-foot-and-mouth disease infection, arteriovenous fistula (AVF) failure, chronic kidney disease (CKD) and breast cancer diagnosis.

“Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country” by Pan et al. [ 5 ], “COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data using Distributed Machine Learning” by Alomari et al. [ 6 ] and “Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network” by Bellocchio et al. [ 7 ] all present research around the COVID-19 pandemic. Pan et al. [ 5 ] identified 24 potential risk factors driving variation in SARS-CoV-2 case fatality rate (CFR). Their model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and the percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR. Alomari et al. [ 6 ] proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) ML and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. Using the tool, they collected a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February to 1 June 2020. They detected 15 government pandemic measures and public concerns, and six macro-concerns (economic sustainability, social sustainability, etc.), and formulated their information-structural, temporal, and spatio-temporal relationships. Bellocchio et al. [ 7 ] present a sentinel surveillance system supported by an ML prediction model, whereby the occurrence of COVID-19 cases in a clinic propagates distance-weighted risk estimates to adjacent dialysis units. The system allows for a prompt risk assessment and a timely response to the challenges posed by the COVID-19 epidemic throughout Fresenius Medical Care (FMC) European dialysis clinics.

“Sharing Is Caring-Data Sharing Initiatives in Healthcare” by Hulsen [ 8 ] shows an analysis of the current literature around data sharing, and discusses five aspects of data sharing in the medical domain, namely publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. With federated data, there is no need for a centralized source database (with all its privacy issues), because the algorithm is brought to the data instead of the other way around. The author also discusses some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

“Digital Training for Non-Specialist Health Workers to Deliver a Brief Psychological Treatment for Depression in Primary Care in India: Findings From a Randomized Pilot Study” by Muke et al. [ 9 ] evaluates the feasibility and acceptability of a digital program for training non-specialist health workers to deliver a brief psychological treatment for depression. This study, performed in Sehore (a rural district in Madhya Pradesh, India) adds to mounting efforts aimed at leveraging digital technology to increase the availability of evidence-based mental health services in low-resource primary care settings in.

“Association of Metabolically Healthy Obesity and Future Depression; Using National Health Insurance System Data in Korea from 2009–2017” by Seo et al. [ 10 ] investigates if depression and metabolic status are relevant by classifying them into the following four categories by their metabolic status and body mass index: (1) metabolically healthy non-obese (MHN); (2) metabolically healthy obese (MHO); (3) metabolically unhealthy non-obese (MUN); and (4) metabolically unhealthy obese (MUO). Their results show that the MHN ratio in women is higher than in men. In both men and women, depression incidence was the highest among MUO participants. In female participants, MHO is also related to a higher risk of depressive symptoms. This indicates that MHO is not an entirely benign condition in relation to depression in women. Therefore, reducing the number of metabolic syndrome and obesity patients in Korea will likely reduce the incidence of depression.

“Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico” by Rojas-Mendizabal et al. [ 11 ] aims to determine the correlated variables with thoracic pain of cardiac origin. Their analysis of 258 geriatric patients from Medical Norte Hospital in Tijuana (Baja California, Mexico) uses two ML techniques, i.e., tree classification and cross-validation. Their results suggest that among the main factors related to cardiac thoracic pain are dyslipidemia, chronic kidney failure, hypertension, diabetes, smoking habits, and troponin levels at the time of admission.

“Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model” by Lin et al. [ 12 ] discusses the high number of recent infections of hand-foot-and-mouth disease (HFMD). Previous research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of this disease is ignored. Existing early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to provide early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses ML methods to establish an HFMD epidemic prediction model with a high accuracy. Both incidence data and environmental (mostly weather) data are used.

“Development and Validation of a Machine Learning Model Predicting Arteriovenous Fistula Failure in a Large Network of Dialysis Clinics” by Ricardo et al. [ 13 ] derived and validated an arteriovenous fistula failure model (AVF-FM) based on ML. The model was trained in the derivation set (70% of initial cohort) by exploiting the information routinely collected in the Nephrocare European Clinical Database (EuCliD; 13,369 patients). Model performance was tested by concordance statistic and calibration charts in the remaining 30% of records. Feature importance was computed using the SHapley Additive exPlanations (SHAP) method. The model achieved good discrimination and calibration properties by combining routinely collected clinical and sensor data, requiring no additional effort by healthcare staff. Therefore, it can potentially facilitate risk-based personalization of AVF surveillance strategies.

In “Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD)” by Ricardo et al. [ 14 ] a novel algorithm predicting end-stage kidney disease (ESKD) is described, named PROGRES-CKD. This Naïve-Bayes classifier accurately predicts kidney failure onset among chronic kidney disease (CKD) patients. Contrary to equation-based scores, PROGRES-CKD extends to patients with incomplete data and allows for the explicit assessment of prediction robustness in case of missing values. The algorithm may efficiently assist physicians’ prognostic reasoning in real-life applications.

Finally, Rasool et al. [ 15 ] discuss in “Improved Machine Learning-based Predictive Models for Breast Cancer Diagnosis” four different predictive models to improve breast-cancer diagnostic accuracy, as well as data exploratory techniques (DET) such as feature distribution, correlation, elimination and hyperparameter optimization. The Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets were used as input. They report a significant improvement in the models’ diagnostic capability with their DET. Therefore, the techniques can help to improve breast cancer diagnosis.

The manuscripts in this Special Issue give us only a brief overview of the wide use of data science in healthcare, and offer a glimpse into the future, where even faster computers and more advanced AI algorithms will make many more applications possible. For example, whereas many AI algorithms only use data from specific data types, this can be expanded to a combination of a wide range of patient-related (structured or unstructured) data, including clinical data, imaging data, digital pathology data, genomics data, data from wearables, and much more, to optimize the result for the patient. AI systems will not replace clinicians on a large scale, but rather will support their care for patients [ 16 ]. For example, AI can also be used to optimize the workflow in the hospital, or to create intelligent chatbots to help patients while reducing the workload for the clinicians. Furthermore, AI algorithms created in these times of COVID-19 might be of good use when managing similar pandemics in the future. It is probably safe to say that in ten years from now, there will not be a ‘Data Science in Healthcare’ Special Issue, because by that time almost everything in healthcare will be influenced by data science.

This research received no external funding.

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Top 20 Data Science Research Topics and Areas For the 2020-2030 Decade

In this decade, Data science seems to be the leading field of study because of the numerous opportunities it offers in terms business and financial solutions. Using Machine learning or deep learning approaches as a data scientist will leverage your skills above others thus making you competitive for the decade. In addition, the expertise in these areas puts you in a good position to secure a good job privately, publicly or as a consultant in respective areas. This paper should help you understand the opportunities that this decade brings in terms of research topics and areas for the data scientist or data analysts.

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Computer Science > Computation and Language

Title: phi-3 technical report: a highly capable language model locally on your phone.

Abstract: We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The innovation lies entirely in our dataset for training, a scaled-up version of the one used for phi-2, composed of heavily filtered web data and synthetic data. The model is also further aligned for robustness, safety, and chat format. We also provide some initial parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on MT-bench).

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Innovation and Sustainability Through the Lens of Data Science

Kate Ascher Frames the Discussion

Professor Kate Ascher of Architecture, Planning and Preservation, opened the session. With a background spanning real estate, urban planning, transportation, infrastructure, sustainability, and economic development, Professor Ascher was well positioned to lead conversation exploring the challenges and opportunities in two major areas of climate change discussions: the fuels with which we run our world, and the energy consumption and emissions of buildings in an increasingly urban planet. 

Douglas Almond:  “Methane Spikes when US Liquid Natural Gas Unloaded in Europe”

In terms of climate change, unburnt methane creates 86 times more global warming than an equivalent amount of carbon dioxide. And while methane emissions were steady 20 years ago, the growth in methane emissions is increasing every year, at a faster rate than CO 2 . Much of the reason is the increase in U.S. production of natural gas, largely due to fracking. 

Professor Almond focused on methane leakage from U.S. deliveries of liquified natural gas from the U.S. to Europe via ship. Examining satellite images from over 1,000 U.S. voyages between 2019 and and 2022, and just under 9,000 voyages from other ports, and combining that data with satellite data on methane emissions at European loading facilities, he established that there is a marked increase in methane emissions associated with the U.S. vessels. 

Curiously, there isn’t a similar leakage from vessels originating from other nations, such as Russia or Australia, a phenomenon whose cause hasn’t yet been identified. Different ship configurations, the nitrogen content of the U.S. product, or the nature of the loading contracts are among the areas for further study, he noted during the Q&A part of the session.

“These leakage rates are high,” Professor Almond said of the data from U.S. ships. “As we think about the energy transition, perhaps we shouldn’t consider natural gas as an ideal transition fuel on the way to renewables because of its climate impact, which is much worse than we thought even five years ago.”

Douglas Almond is Professor of Economics and International and Public Affairs at the School of International and Public Affairs.

Bianca Howard: “Decision-Making for Building Decarbonization”

In dense urban environments like New York City, buildings account for 70% of greenhouse gas emissions. To reduce emissions, Bianca Howard, who leads the Building Energy Research Lab, leverages data science to determine how best to reduce the carbon footprint of buildings in a tailored way, exploring options to increase the use of renewable energy sources, along with local storage, demand management, and thermal resilience to improve temperature regulation, and building operation that is responsive to the needs of the larger electrical grid. It’s a challenge that calls for much engineering modeling, optimization, and data science. 

“The goal we’re trying to achieve is quite complex,” said Professor Howard. Beyond these physical requirements, successful conversion, as required by New York City law by 2030, must also consider “decarbonization pathways” that are acceptable to residents in different types of urban buildings with different social and community needs. 

Professor Howard detailed plans for a system of engineering design, implementation, and optimization, with additional goals including green job creation, equity in approach, and an alignment of incentives across different interest groups. The objective is creation of a range of diverse options for building decarbonization from which people can choose their appropriate solution. For this problem, data science can speed information gathering and learning, deep neural networks to improve system optimization, and algorithmic decision trees to indicate the best solution for a particular group. 

Professor Howard also discussed approaches to aid decision-making in real-time for comfortable, efficient, resilient and low carbon operations. Reinforcement learning agents, at first in simulated environments, can provide a basis for better actions in the physical environment as buildings are asked to deliver more complex tasks.

“Our preliminary data is really encouraging,” she said. “We’re able to use very simplified models and still have good control performance, which is really important for developing these buildings.”

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New algorithm cuts through 'noisy' data to better predict tipping points

Mathematicians' theory determines which data points matter most when calculating early warning signals.

Whether you're trying to predict a climate catastrophe or mental health crisis, mathematics tells us to look for fluctuations.

Changes in data, from wildlife population to anxiety levels, can be an early warning signal that a system is reaching a critical threshold, known as a tipping point, in which those changes may accelerate or even become irreversible.

But which data points matter most? And which are simply just noise?

A new algorithm developed by University at Buffalo researchers can identify the most predictive data points that a tipping point is near. Detailed in Nature Communications , this theoretical framework uses the power of stochastic differential equations to observe the fluctuation of data points, or nodes, and then determine which should be used to calculate an early warning signal.

Simulations confirmed this method was more accurate at predicting theoretical tipping points than randomly selecting nodes.

"Every node is somewhat noisy -- in other words, it changes over time -- but some may change earlier and more drastically than others when a tipping point is near. Selecting the right set of nodes may improve the quality of the early warning signal, as well as help us avoid wasting resources observing uninformative nodes," says the study's lead author, Naoki Masuda, PhD, professor and director of graduate studies in the UB Department of Mathematics, within the College of Arts and Sciences.

The study was co-authored by Neil Maclaren, a postdoctoral research associate in the Department of Mathematics, and Kazuyuki Aihara, executive director of the International Research Center for Neurointelligence at the University of Tokyo.

The work was supported by the National Science Foundation and the Japan Science and Technology Agency.

Warning signals connected via networks

The algorithm is unique in that it fully incorporates network science into the process. While early warning signals have been applied to ecology and psychology for the last two decades, little research has focused on how those signals are connected within a network, Masuda says.

Consider depression. Recent research has considered it and other mental disorders as a network of symptoms influencing each other by creating feedback loops. A loss of appetite could mean the onset of five other symptoms in the near future, depending on how close those symptoms are on the network.

"As a network scientist, I felt network science could offer a unique or perhaps even improved approach to early warning signals," Masuda says.

By thoroughly considering systems as networks, researchers found that simply selecting the nodes with highest fluctuations was not the best strategy. That's because some selected nodes may be too closely related to other selected nodes.

"Even if we combine two nodes with nice early warning signals, we don't necessarily get a more accurate signal. Sometimes combining a node with a good signal and another node with a mid-quality signal actually gives us a better signal," Masuda says.

While the team validated the algorithm with numerical simulations, they say it can readily be applied to actual data because it does not require information about the network structure itself; it only requires two different states of the networked system to determine an optimal set of nodes.

"The next steps will be to collaborate with domain experts such as ecologists, climate scientists and medical doctors to further develop and test the algorithm with their empirical data and get insights into their problems," Masuda says.

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Materials provided by University at Buffalo . Original written by Tom Dinki. Note: Content may be edited for style and length.

Journal Reference :

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Science Objectives

To contribute to the four Mars exploration science goals and meet its specific goal of determining Mars' habitability, Curiosity has the following science objectives:

Biological objectives

Geological and geochemical objectives, planetary process objectives, surface radiation objective.

1. Determine the nature and inventory of organic carbon compounds 2. Inventory the chemical building blocks of life (carbon, hydrogen, nitrogen, oxygen, phosphorous, and sulfur) 3. Identify features that may represent the effects of biological processes

1. Investigate the chemical, isotopic, and mineralogical composition of the Martian surface and near-surface geological materials 2. Interpret the processes that have formed and modified rocks and soils

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With over a decade of exploration, Curiosity has unveiled the keys to some of science's most unanswered questions about Mars. Did Mars ever have the right environmental conditions to support small life forms called microbes? Early in its mission, Curiosity's scientific tools found chemical and mineral evidence of past habitable environments on Mars. It continues to explore the rock record from a time when Mars could have been home to microbial life.

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

• Age & Generations
• Demographic Research
• Generation X
• Generation Z
• Generations
• Greatest Generation
• Methodological Research
• Millennials
• Silent Generation

Kim Parker is director of social trends research at Pew Research Center

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