Data science has changed almost every industry. In medicine, algorithms help predict patient side effects. In sports, models and metrics have redefined “athletic potential.” Data science has even tackled traffic, with route-optimization models that capture typical rush hours and weekend lulls.
Below we rounded up examples of data science applications seen today in areas ranging from e-commerce to healthcare.
Data Science Applications and Examples
- Healthcare: Data science identifies and predicts disease, and personalizes healthcare recommendations.
- Transportation: Data science optimizes shipping routes in real-time.
- Sports: Data science accurately evaluates athletes’ performance.
- Government: Data science prevents tax evasion and predicts incarceration rates.
- E-commerce: Data science automates digital ad placement.
- Gaming: Data science improves online gaming experiences.
- Social media: Data science creates algorithms to pinpoint compatible partners.
- Fintech: Data science creates credit reports and financial profiles.
Healthcare Data Science Applications
In 2008, Google staffers created Google Flu Trends, a tool that mapped flu outbreaks in real time by tracking location data on flu-related searches. But it didn’t work. In 2013, Google estimated about twice the flu cases that were actually observed (the Flu Trends algorithm relied too much on correlations between search term volume and flu cases). Even so, it demonstrated the potential and importance of data science in healthcare. Here are some examples to know.
1. Identifying Cancer Tumors
Google developed a tool called LYNA for identifying breast cancer tumors that metastasize to nearby lymph nodes. That can be difficult for the human eye to see, especially when the new cancer growth is small. In one trial, LYNA — short for Lymph Node Assistant — accurately identified metastatic cancer 99 percent of the time using its machine-learning algorithm. More testing is required, however, before doctors can use it in hospitals.
2. Tracking Menstrual Cycles
The popular Clue app employs data science to forecast users’ menstrual cycles and reproductive health by tracking cycle start dates, moods, stool type, hair condition and other metrics. Behind the scenes, data scientists mine this wealth of anonymized data with tools like Python and Jupyter’s Notebook. Users are then algorithmically notified when they’re fertile, on the cusp of a period or at an elevated risk for conditions like an ectopic pregnancy.
3. Personalizing Treatment Plans
Oncora’s software uses machine learning to create personalized recommendations for current cancer patients based on data from past ones. Healthcare facilities using the company’s platform include UT Health San Antonio and Scripps Health. Doctors can spend up to two-thirds of their time working on clinical documentation, so employing Oncora’s platform helped cut down on paperwork and enabled doctors to be more efficient.
4. Cleaning Clinical Trial Data
Veeva is a cloud software company that provides data and software solutions for the healthcare industry. The company’s reach extends through clinical, regulatory and commercial medical fields. Veeva’s Vault EDC uses data science to clean clinical trial findings and help medical professionals make adjustments mid-study.
Transportation and Logistics Data Science Examples
While both biking and public transit can curb driving-related emissions, data science can do the same by optimizing road routes. And though data-driven route adjustments are often small, they can help save thousands of gallons of gas when spread across hundreds of trips and vehicles. Here are some examples of data science hitting the road.
5. Modeling Traffic Patterns
StreetLight uses data science to model traffic patterns for cars, bikes and pedestrians on North American streets. Based on a monthly influx of trillions of data points from smartphones, in-vehicle navigation devices and more, Streetlight’s traffic maps stay up-to-date. They can even identify groups of commuters who use multiple transit modes to get to work, like a train followed by a scooter. The company’s maps inform various city planning enterprises, including commuter transit design.
6. Optimizing Food Delivery
The data scientists at UberEats have a fairly simple goal: getting hot food delivered quickly. Making that happen across the country, though, takes machine learning, advanced statistical modeling and staff meteorologists. In order to optimize the full delivery process, the team has to predict how every possible variable — from storms to holiday rushes — will impact traffic and cooking time.
7. Improving Package Delivery
UPS uses data science to optimize package transport from drop-off to delivery. The company’s integrated navigation system ORION helps drivers choose over 66,000 fuel-efficient routes. ORION has saved UPS approximately 100 million miles and 10 million gallons of fuel per year with the use of advanced algorithms, AI and machine learning. The company plans to continue to update its ORION system, with the last version having been rolled out in 2021. The latest update allowed drivers to reduce their routes by two to four miles.
8. Enhancing Supply Chain Management
XPO Logistics has embraced a data analytics-first approach to manage ingoing and outgoing orders more effectively. With historical shipment data, the company can predict where it will need to build infrastructure to support more orders and allocate human personnel accordingly. In addition, XPO Logistics has replaced manual data entry with scanners that help workers determine where shipments need to go within the company’s supply chain network.
Sports Data Science Applications
In the early 2000s, the Oakland Athletics’ recruitment budget was so small the team couldn’t recruit quality players. So the general manager redefined quality, using in-game statistics other teams ignored to predict player potential, assemble a strong team and make the playoffs despite the team’s budget.
Author Michael Lewis wrote a book about the phenomenon, Moneyball. Since then, the global market for sports analytics has thrived and is expected to reach 8.4 billion by 2026. Here are some examples of how data science is transforming sports.
9. Extracting Performance Metrics From Video
Strong Analytics is a team of technology experts who collaborate with client businesses on building custom data science and machine learning solutions. Its portfolio of projects includes creating a computer vision system capable of analyzing pre-recorded competition footage to produce detailed metrics that can inform coaching and help improve athlete performance.
10. Making Predictive Insights in Basketball
RSPCT’s shooting analysis system, adopted by NBA and college teams, relies on a sensor on a basketball hoop’s rim equipped with a tiny camera that tracks exactly when and where the ball strikes on each basket attempt. It funnels that data to a device that displays shot details in real time and generates predictive insights.
“Based on our data… We can tell [a shooter], ‘If you are about to take the last shot to win the game, don’t take it from the top of the key, because your best location is actually the right corner,’” RSPCT COO Leo Moravtchik told SVG News.
11. Tracking Physical Data for Athletes
WHOOP makes wearable devices that track athletes’ physical data like resting heart rate, sleep cycle and respiratory rate. The goal is to help athletes understand when to push their training and when to rest — and to make sure they’re taking the necessary steps to get the most out of their body. Professional athletes like Olympic sprinter Gabby Thomas, Olympic golfer Nelly Korda and PGA golfer Nick Watney are among WHOOP’s users.
12. Gathering Performance Metrics for Soccer Players
Trace provides soccer coaches with recording gear and an AI system that analyzes game film. Players wear a tracking device while its specially designed camera records the game. The AI bot then takes that footage and stitches together all of the most important moments in a game. This technology allows coaches and players to have more detailed insights from game film. Beyond stitching together clips, the software also provides performance metrics and a field heat map.
13. Informing Sports Broadcasts and Outlets
Sportradar compiles data from sporting events to allow broadcast teams to update their graphics with the latest data and provide timely stats to keep audiences engaged. Editorial teams can also leverage Sportradar’s platform to supplement game summaries and compelling storylines with meaningful statistics.
Government Data Science Applications
U.S. government agencies can access heaps of data. Not only do they maintain their own databases of ID photos, fingerprints and phone activity, but government agents can also get warrants to obtain data from any American data warehouse. Here are some of the ways government agencies apply data science to vast stores of data.
14. Predicting Recidivism Within Incarcerated Populations
Equivant’s Northpointe software suite attempts to gauge an incarcerated person’s risk of reoffending. Its algorithms predict that risk based on a questionnaire that covers the person’s employment status, education level and more. No questionnaire items explicitly address race, but ProPublica found that the Equivant algorithm pegs Black people as higher recidivism risks than white people 77 percent of the time — even when they’re the same age and gender, with similar criminal records. ProPublica also found that Equivant’s predictions were 71 percent accurate.
15. Mining Databases With Facial Recognition Software
The U.S. Immigrations and Customs Enforcement has used facial recognition technology to mine driver’s license photo databases, with the goal of deporting undocumented immigrants. The practice — which has sparked criticism from both an ethical and technological standpoint — falls under the umbrella of data science. Facial recognition builds on photos of faces with AI and machine learning capabilities.
16. Detecting Tax Fraud
Tax evasion among wealthy Americans costs the U.S. government over $150 billion per year, so it’s no wonder the IRS has modernized its fraud-detection protocols in the digital age. To the dismay of privacy advocates, the agency has constructed multidimensional taxpayer profiles from public social media data, assorted metadata, emailing analysis, electronic payment patterns and more. Based on those profiles, the agency forecasts individual tax returns — anyone with wildly different real and forecasted returns gets flagged for auditing.
Gaming Data Science Examples
Data science and AI have been used in video games for decades. You see it in Pac-Man, where they were used in the game’s mazes and to give the ghosts distinct personalities. The video game industry continues to find creative ways to implement data science and AI to improve game play and entertain millions of people across the globe, swelling to over $244 billion in 2024. Here are just a few examples of how data science is used in video games.
17. Improving Online Gaming
Known for games like Call of Duty, World of Warcraft, Candy Crush and Overwatch, Activision Blizzard uses big data to improve their online gaming experiences. One example of this being the company’s game science division analyzing gaming data to prevent empowerment — the attempt to improve someone else’s sports scores through negative means — among Call of Duty players. The company also uses machine learning to detect power boosting and identify and track key indicators for increasing quality of game time.
18. Making Suggestions to Gamers to Improve Play
2K Games is a video game studio that has created popular titles like Bioshock and Borderlands, as well as both the WWE and PGA games series. The company’s game science team focuses on extracting gaming data and building models in order to improve its sports games like NBA2K. Data scientists at 2K games analyze player gameplay and economy telemetry data to understand player behavior and suggest actions to improve the player experience.
19. Monitoring Business Metrics in the Video Game Industry
Unity is a platform for creating and operating interactive, real-time 3D content, including games. The platform is used by gaming companies like Riot Games, Atari and Respawn Entertainment. Unity uses gaming data to make data-driven decision-making within its product development team and to monitor business metrics.
20. Creating Life-Like Characters
Electronic Arts (EA) is using data science to develop more realistic gaming experiences, as demonstrated in games like EA Sports FC 24. Through a motion capture technology known as HyperMotion V, the company collected data on real-life professional soccer players, which allowed in-game versions to replicate the players’ distinct movements. This data also enables in-game players to take on the characteristics of actual players, requiring users to be more mindful when seeking to maximize each player’s unique skill set.
E-Commerce Data Science Applications
Online retailers often automatically tailor their web storefronts based on viewers’ data profiles. That can mean tweaking page layouts and customizing spotlighted products, among other things. Some stores may also adjust prices based on what consumers seem able to pay, a practice called personalized pricing. Even websites that sell nothing feature targeted ads.
Here are some examples of companies using data science to automatically personalize the online shopping experience.
21. Creating Targeted Ads
Sovrn brokers deals between advertisers and online outlets. Since these deals happen millions of times a day, Sovrn has mined a lot of data for insights, which manifest in its intelligent advertising technology. Compatible with Google and Amazon’s server-to-server bidding platforms, its interface can monetize media with minimal human oversight — or, on the advertiser end, target campaigns to customers with specific intentions.
22. Curating Vacation Rentals
Once upon a time, Airbnb prioritized top-rated vacation rentals that were located a certain distance from a city’s center. That meant users could always find beautiful rentals, but not always in cool neighborhoods. Engineers solved that issue by prioritizing the search rankings of a rental if it’s in an area that has a high density of Airbnb bookings. There’s still breathing room for quirkiness in the algorithm, too, so cities don’t dominate towns and users can stumble on the occasional rental treehouse.
23. Predicting Consumers’ Product Interests
Instagram uses data science to target its sponsored posts, which hawk everything from trendy sneakers to influencers posting sponsored ads. The company’s data scientists pull data from Instagram as well as its owner, Meta, which has exhaustive web-tracking infrastructure and detailed information on many users, including age and education. From there, the team crafts algorithms that convert users’ likes and comments, their usage of other apps and their web history into predictions about the products they might buy.
24. Creating Digital Ad Opportunities
Taboola uses deep learning, AI and large data sets to create engagement opportunities for advertisers and digital properties. Its discovery platform creates new monetization, audience and engagement by placing advertisements throughout a variety of online publishers and sites. Its discovery platform can expose readers to news, entertainment, topical information or advice as well as a new product or service.
Social Platform Data Science Examples
The rise of social networks has completely altered how people socialize, with online data helping shape how relationships begin and develop in the digital age. Here are some examples of data science fostering human connection.
25. Curating Matches on Dating Apps
When singles match on Tinder, they can thank the company’s data scientists. An algorithm works behind the scenes, boosting the probability of matches. Once upon a time, this algorithm relied on users’ Elo scores, essentially an attractiveness ranking. Now, it prioritizes matches between active users, users near each other and users who seem like each other’s “types” based on their swiping history.
26. Suggesting Friends on Facebook
Meta’s Facebook platform uses data science in various ways. One of its buzzier data-driven features is the “People You May Know” sidebar, which is based on a user’s friend list, the people they’ve been tagged with in photos and where they’ve worked and gone to school. It’s also based on “really good math,” according to the Washington Post — specifically, a type of data science known as network science, which essentially forecasts the growth of a user’s social network based on the growth of similar users’ networks.
Fintech Data Science Applications
Fintech and data science go hand in hand, as financial companies typically use insights drawn from raw data to make lending decisions and create credit reports. Data science is also used to predict consumer behavior, run risk evaluations and optimize financial portfolios and assets. Here are some of the companies using data science for fintech applications.
27. Accelerating Underwriting for Life Insurance
Bestow offers life insurance solutions for both individuals and enterprises. The company’s goal is to make life insurance accessible and affordable for everyone. It uses data science to power its accelerated underwriting process, which pulls data from external sources like credit reports, motor vehicle records or the Medical Information Bureau. Accelerated underwriting is helped by data science’s predictive algorithms to determine an applicant’s risk factors.
28. Creating Credit Reports
TransUnion is known for providing credit reports, fraud monitoring services and financial loans. The company’s data science team is responsible for creating predictive models based on data reporting from auto dealers to retailers to mortgage companies. The company uses data science to extract insights from both an individual’s credit data and public record data. These insights are used by financial institutions and lenders to make informed decisions about extending credit offers and loan opportunities.
29. Gathering Payroll Data
Pinwheel uses data science to provide payroll solutions in the banking and lending industries. Pinwheel’s Earnings Stream gives financial institutions time-and-attendance data on their customers, as well as historical payroll data, accrued earnings and projected earnings. The system bases projections on compiled historical data and allows finance companies to stay up to date on their customer’s income and employment history.
Frequently Asked Questions
What are the main uses of data science?
Data science is mainly used to analyze and glean insights from massive volumes of data, so teams can build predictive models based on past trends, create digestible visuals and graphics and train algorithms to enhance their performance.
How is data science applied in real life?
A few real-life applications of data science are detecting cancer tumors, better managing supply chain networks, measuring professional athletes’ in-game performance and developing credit reports, among other use cases.
Where is data science mostly used?
Data science is most often used in industries that leverage data-driven insights to make their operations more efficient and improve the customer experience. These include healthcare, e-commerce and fintech.
