Data Science Applications Across 10 Different Industries

As data becomes increasingly valuable, new applications of data science are emerging across different industries. See how and where data science is used.

MDS Blog 10 Data Science Industries

The growing number of industries collecting information about their products, services, and processes has increased the demand for data scientists and analysts who can interpret that data. Technical recruiter Michael Sabo said, “The biggest trend we’ve seen is with data analytics roles. Over the last decade, we’ve seen a huge increase in hiring for data analysts, data scientists, and data engineers and architects in large and small companies.” For comparison, the average growth from 2021-2031 for all occupations tracked by the U.S. Bureau of Labor Statistics is 5%, but the BLS projected a 36% growth in data scientist roles in this decade.

The term 'data science’ is still new, coined less than two decades ago to describe the distillation of trends and other details from a stream of data points. Machine learning (ML) is required to manage the huge data sets, so analysts must be comfortable with ML algorithms and programming. The two most common programming languages for data scientists are R and Python.

Data scientists can work for any employer collecting and using data, within industries as varied as entertainment and energy. Rice researched various sectors to find emerging applications of data science across industries:

1. Healthcare

2. Financial Services

3. Gaming, Sports, and Entertainment

4. Energy, Oil, and Gas

5. Aerospace

6. Manufacturing, Logistics, and Supply Chain

7. Insurance

8. Management Consulting and Professional Services

9. Transportation and Travel

10. Retail and E-commerce

But what does a data scientist do? Like detectives, data scientists examine clues in the form of data to reveal a story. They often begin with a problem, analyze data to determine potential solutions, and collaborate with their peers across the company to explore, test and confirm their theories.

Learning to mine, prioritize, and assess data can be accomplished through programs such as the Rice University Master of Data Science (MDS) degree, where courses like statistics, programming, data visualization, and machine learning are linked to real world problems and experiences. Employees ready to switch or advance their careers by completing a Rice MDS degree will typically accept offers in industries such as:

Data Science in Healthcare

Data scientists work closely with healthcare professionals to improve diagnoses, treatments, and processes. For example, by combining natural language processing (NLP) with data from scientific texts, data scientists can train chatbots to answer basic patient questions. Data drawn from processes like scheduling appointments, filing insurance claims, and billing is analyzed to increase value-based care, reduce costs, and reduce errors. Additional data science applications in healthcare include:

  • Clinical and Lab Reporting - Data scientists can efficiently speed up the processing of clinical and lab reports and utilize deep machine learning to yield additional diagnosis insights.
  • Examining Medical Images - Advanced data science methods have helped train machines to become more adept at analyzing and identifying anomalies in medical images such as MRIs, X-rays, and scans.
  • Informing Wearable Health Tech - Today’s health tech wearables monitor and treat diseases ranging from diabetes and epilepsy to cancer and strokes. Regardless of the device, healthcare professionals work with data scientists to analyze its data to improve patient outcomes.
  • Map Citizens’ Access to Health Services - As maps tracking the coronavirus pandemic proved, geographic information systems (GIS) can help healthcare workers plot the spread of a disease or identify populations most at risk. Data scientists can leverage data and GIS systems to determine the accessibility of public health services.

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Data Science in Financial Services

The financial services industry, an early adopter of data-informed decision making processes, requires an increasing number of data scientists. Rice OMCS student Keaton Parkinson works as a tax technology and transformation consultant with Ernst and Young.

“There are new data problems to solve every day. We have a group that specializes in machine learning, and we also have quantitative groups that focus on areas of tax with a lot of data. Dealing with this material can include cleaning large amounts of data, scraping large PDFs, storing the information in a database, or providing insights to clients,” said Parkinson.

“As computational power has increased over the past two decades, we've become capable of applying data science methods and techniques that weren't previously feasible. While the foundational principles of data science remain the same, many of the techniques and methods we're using today –especially in the accounting industry– are relatively new. As such, maintaining an innovative mindset is crucial.”

Some of the financial services applications that now rely on data science applications include:

  • Assessing Risk - In addition to its use in calculating the risk of a proposed investment or transaction, data science drives the logic that blocks a fraudulent charge or initiates a text message about an unusual purchase amount or location.
  • Managing Portfolios - Algorithms and data analysis help tailor investment portfolios to retain a client’s preferences and attain their goals while responding to a fluctuating market.
  • Calculating Customer Lifetime Value - For banks and their customers, some relationships are healthier than others. Data analysis can help predict the expected lifetime value of a customer, prompting special offers or other incentives to maximize profitability.
  • Improving Customer Experience with Natural Language Processing - Like healthcare and other service providers, the finance industry is deploying more data-driven NLP chatbots to supply quick answers rather than put their clients on hold for the next available representative.

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Data Science in Gaming, Sports, and Entertainment

Moneyball introduced the public to the idea of creating a winning baseball team by choosing players based on data-driven computer-aided analyses of their statistical data. The growth of data science applications expanded to other forms of competitive entertainment such as the casino and betting industries and online gaming. Zynga, Electronic Arts, and Activision - Blizzard Media are a few of the top gaming companies hiring data scientists.

Some of the gaming and e-sports applications that rely on data science skills include:

  • Casinos and Big Data - Risk management may be the largest application for data science in casinos and sports betting, but protecting their customers’ privacy and improving their play experiences are also critical to successful companies in this area of the entertainment industry. For online casinos in particular, the analysis of behavioral data like active and inactive clients, length of play, and gaps in activity can be used to shape more personalized experiences for each user.
  • Detecting Fraud in Online Gaming Accounts - Online games ranging from Words With Friends to Call of Duty offer their users a variety of play and investment levels as well as the opportunity to play against AI opponents. Data analysis supports the continuous evolution required to maintain optimal player satisfaction as the user’s skills improve, but the game companies are also concerned with fair play. Boosting and other methods of cheating decrease user satisfaction and may result in financial losses for more authentic users, so the use of data science to detect and prevent gaming fraud is becoming more prevalent.

Rice alumna Emily Robinson, author of the book "Build a Career in Data Science" and a senior data scientist at Game Data Pros, is fascinated by every aspect of data in the game industry.

Robinson said, “I’m excited to apply my experience in A/B Testing, machine learning, and analytics to a new industry. Video games is a huge market - valued at $160 billion in 2020, it’s bigger than the music and movie industry combined! More than 3.2 billion people play video games, and working at GDP I get to analyze billions of data points and positively impact millions of players. My team creates a foundation of organized, comprehensive, accurate, and accessible data, and then uses advanced statistics to understand and predict player behavior.”

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Data Science in Energy, Oil, and Gas

The energy industry provides many career opportunities for data analysts, data engineers and data scientists. Rice MCS alumna Andrea Pound began working with Schlumberger’s Industrial IOT (IIOT) cloud data in 2015. She said, “It’s surprising how much we’ve learned about what’s happening deep underground. Utilizing IIoT technologies at a well site is like turning on a flashlight in a dark room.”

Oil and gas data scientists may also utilize GIS data. Every phase of the industry – from exploration to a final consumer product – benefits from data science applications, including:

  • Predicting Maintenance and Improving Safety - At the 2023 Offshore Technology Conference in Houston, representatives from Oxy and Shell spoke about their use of AI and data analysis to better predict maintenance and to improve safety in operations by proactively monitoring systems for anomalies.
  • Identifying Ideal Drilling Sites – Data scientists focused on exploration aspects of the energy industry can help their employers maximize current drilling sites and identify new sites with analyses like reservoir characterization.

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Data Science in Aerospace

The space flight industry needs data scientists in both traditional organizations like NASA and disruptors like SpaceX, a private company that imagined reusable rockets and sped their ships to launch with investments including cutting edge technology and improved data management. For airlines ferrying passengers and cargo, data scientists develop models to predict weather patterns and analyze previous flight data to inform evolving engineering decisions.

  • Tracking Aircraft Damage – Like IOT devices used in the energy industry, sensors on aircraft and spaceships record data that is analyzed to help develop a more efficient fleet, improve rocket technology, and track assets. Analyzing sensor data to monitor fatigue and other vulnerabilities that might result in damage is another application of data science in the airlines.
  • Predictive Maintenance – About one-third of flight delays are due to unscheduled maintenance but data from IOT sensors can be used to better predict where trouble is likely to occur. Data science applications deliver information to technicians in real-time and send system notifications to ensure the right tools and parts are available for the work.

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Data Science in Manufacturing, Logistics, and Supply Chain

Digital transformation for companies in the manufacturing industry has prompted the Fourth Industrial Revolution (I4), and the most successful Industry 4.0 leaders leverage not just IOT sensors and data science in manufacturing, but also artificial intelligence (AI) and a surge in autonomous robots and vehicles. Data scientists help their teams boost productivity by closely monitoring streams of data from all these sources to identify evolving trends in their business. Closely linked with manufacturing, companies that manage logistics and various aspects of the supply chain are also dependent on data science applications.

  • Improving Production and Distribution Efficiencies - The use of data science tools like ML and predictive algorithms can speed up production time, avoid processing delays, and schedule preventative maintenance. Data science is also used in quality assurance, storage and packaging efficiencies, and supply chain logistics.

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Data Science in Insurance

Insurers depend on the analyses of both actuaries and data scientists to remain in business. One of the largest threats to insurers and their customers is the cost of fraudulent claims; paying for damage or a health procedure that never occurred reduces the amount of funds available for legitimate claims. In addition to developing models to detect insurance fraud, data scientists also use ML and AI models to process simple claims, address membership questions, and identify marketing opportunities for customers celebrating life-time events like a new baby.

The master's degree in data science at Rice University equips its graduates with the tools and skills necessary to tackle valuable applications such as:

  • Predicting Claims - Insurers must not only respond to claims but also reevaluate the appropriate rate and coverage when an area is impacted by recurring crises like wildfires or flooding. Using previous customer and local data, historical comparisons for similar areas, and other risk management factors, data scientists can better predict the cost and profitability of revised insurance policies.
  • Personalizing Policy Options - Consumers celebrating events like a wedding, new car, or adoption may overlook the need for new or updated insurance coverage. To reach these potential clients, automated marketing tools shaped by data scientists can match products to changes in consumers’ lives, drawing attention to features and price options that resonate with other clients in similar market segments.

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Data Science in Management Consulting and Professional Services

McKinsey, Boston Consulting Group, and other management consulting firms create tailored solutions for clients who request assistance solving problems such as profit downturn for a particular product, bottlenecks in a manufacturing or distribution system, or too few candidates advancing through the hiring process. This type of business analysis is uniquely suited to data science consultants who address challenges like:

  • Inform and Validate Managerial Decisions - By tapping into a company’s existing data, consultants can identify likely causes of the problem they’ve been hired to resolve. The consultants present their findings and recommendations to the client’s managerial team and may remain to help implement the changes, or move on to the next consulting challenge. The continuous stream of new challenges to solve is often one of the attractions for data scientists who join these firms.
  • Recruiting - Application Tracking Systems (ATS) are one of the sources of information data science consultants use to help a client improve their recruiting results. Examples of recruiting analysis include identifying why the client does not attract adequate qualified applicants and/or determining where in the process favored candidates withdraw their application.

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Data Science in Travel and Transportation

From Amazon deliveries to subway rides, applications for data science in the transportation industry help move products and people efficiently and safely to their destinations. For example, models that predict the number of travelers, traffic congestion, and weather can be used by airports and transit centers to determine gate activity and resource requirements like baggage handlers, mechanics, and fuel.

  • Estimating Travel Time - Many mobile devices include map applications that use data science models to estimate travel time and suggest not only various routes but also travel time options for air travel, driving, walking, or public transit.
  • Autonomous Delivery - The grocery giant Kroger and the new self-driving vehicle manufacturer Nuro have teamed up to launch autonomous grocery deliveries in several U.S. locations. Both companies depend on data scientists and engineers for their success. Data science models for self-driving vehicles include inputs like road signs, parked cars and pedestrians as well as data from the vehicle’s sensors and vehicle-to-vehicle cloud communications.

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Data Science in Retail and E-commerce

When it comes to selling consumer products, data science in retail and the e-commerce industry is used to help companies attract customers and avoid empty shelves or sluggish inventories.

  • Stocking Inventory to Match Supply and Demand - Balancing customer preferences with optimized pricing while maintaining an adequate inventory is a challenge best met with big data. Analyzing the global and local availability of raw materials and other resources, tracking statistics to reveal current supply chain issues, and monitoring customer purchasing behavior can help a company achieve their inventory and distribution goals.
  • Recommendation Systems - Shopping sites and services use algorithms and machine learning tools like NLP and data visualization to predict trends and refine the prompts in their recommendation systems to match customer interests and purchases.

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Bolster Your Data Science Portfolio with a Master’s in Data Science (Online) from Rice University

There are more opportunities for data scientists beyond the areas mentioned here. Data science has applications in every industry that collects data. Prepare for a career analyzing data in an industry you love like entertainment or one you feel called to support like healthcare or renewable energy. The MDS online program at Rice prepares data scientists for careers in a wide range of industries by incorporating real world problems in courses like programming, machine learning, statistics and data visualization.

Data Science employment is projected to grow +35% between 2022-2032.

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