Data Science Examples

Data Science > Examples

Data science, once a niche discipline utilized strictly by specialized statisticians and mathematicians, has evolved over the last 60 years into an invaluable field leveraged world-wide by average users to turn data into useful information for a vast array of applications. 

Data science use cases have exploded, including everything from biological sciences and healthcare to medical informatics and social sciences. Data science now influences decision making in economics, government, business, and finance sectors. And as artificial intelligence and machine learning become increasingly integrated into our daily lives, the use of data science is only expected to grow.  

The following are some useful data science examples:

Data Science in Business

The impact of data science in business is significant. Data science combines data with technology and algorithm building to power business intelligence and determine how a wide range of factors might affect a business, and what the needs of their customers are based on existing data. For example, data scientists can create models on past browsing history and purchase history to provide more accurate search results for customers. The ultimate goal of data science in business is to gain insight into customer behavior, improve the customer experience, and make better business decisions. 

Data science examples in business include processes such as aggregating a customer’s email address, credit card information, social media handles, and purchase identifications in order to identify trends in their behavior. Collecting and analyzing data on a larger scale, such as tracking search engine queries and purchase data, can also help business leaders predict future market trends. Another use case is automating the recruitment process. Analytical algorithms like clustering and classification can help narrow down and churn out the best candidate for the job.

Data Science in Healthcare

Data science in medicine and healthcare is crucial. Healthcare systems generate enormous volumes of structured, unstructure, and fragmented data, such as patient demography, treatment plans, results of medical examinations, and insurance. Applying data science to big data in the pharmaceutical industry enables a means to assimilate, process, manage, and analyze this data.

Some applications of data science in healthcare include: monitoring patient health with wearable sensors or home devices; predicting, tracking, and preventing chronic diseases at an early level; providing virtual assistance for patients with the help of disease predictive modeling; monitoring the logistic supply of hospitals and pharmaceutical departments; identify various correlations and association of symptoms, finds habits, diseases and then makes meaningful predictions; and improving drug discovery through clinical trial analysis

Data science use cases in healthcare are also related to cost. According to McKinsey, applying data science to the US healthcare system could reduce healthcare fraud, waste and abuse costs by $300 billion to $450 billion, or 12 to 17 percent of its total cost.

Data Science in Finance

Data science in the finance industry can be used to help reduce non-performing assets by  identifying downward trends sooner. For example, institutions might use data science for quantitative financial modeling that can perform predictive analytics on customer payment history data in order to limit the probability of customers defaulting on loan or credit payments. The institution could predict the timeliness or likelihood of future payment.

Other data science use cases in finance include: providing personalized services with the use of speech recognition and natural language processing-based software; measuring customers’ lifetime value, increasing cross-sales, and decreasing “below zero” customers with alternative data analysis; accelerating financial analytics to identify unusual patterns and anomalies in trading data; improving fraud detection in investment management; developing new trading strategies with the use of machine learning algorithms; and tracking transactions, credit scores and other financial attributes in real-time.

Data Science in Marketing

Data science is transforming marketing and plays a major role in helping sales and marketing teams gain insight into consumer behavior and purchasing patterns.

Data science in sales and marketing is used for things like: market forecasting, identifying new customer base, refining and optimizing pricing and budget, analyzing customer portfolios, predicting future patterns, and identifying actions that could meaningfully affect overall business strategy, utilizing sentiment data analysis in order to create and maintain a positive reputation for the business, and utilizing recommendation engines to recommend relevant products to customers. Additionally, Analyzing big data in the media industry gives media planners, user acquisition managers, and advertising data analysts the ability to rapidly understand and visualize the effectiveness and value of ads and ad spend.

Some data science use cases in marketing include: AirBnB, which relies heavily on data science projects like AirFlow and AirPal to discover new growth opportunities and learn more about users and potential customers; Netflix, which leverages a recommendation engine to suggest new films and series based on the viewing history of users with similar interests; and Spotify, which, similar to Netflix, uses a recommendation engine for their Discover Weekly playlist and Release Radar to keep their customers subscribed and coming back for more. Data science in the entertainment industry and churn prediction data science will only continue to grow as online streaming platform options increase and competition grows.

Data Science in Cyber Security

A major contribution that data science brings to cybersecurity is anomaly detection. Cyber attacks are more often than not perpetrated with the use of code that looks or behaves differently than the norm. The rise of big data analytics in cyber defense helps us create Machine Learning (ML) models that can detect and create alerts for anomalous activity before it develops into a full-on attack. 

Anomaly detection data science is a reactive technique of data science for fraud detection. Data science can also be used proactively in penetration testing. Penetration testing trains ML models to adapt from previous experiences and effectively test firewalls for weaknesses. Attacks launched by code must be met with algorithmic detection. Waiting for a human being to notice that something has gone wrong puts millions of financial records, health records, and security credentials at risk. 

Data Science in Insurance

Data science in insurance is all about risk assessment, pricing, and fraud detection. Between auto, health, property, and business insurance, the insurance industry is composed of vast and varied datasets that data science can help transform into useful information. Insurance data scientists and data analysts combine analytical applications with a continuous stream of real-time data to create personalized, thorough risk assessments. Fraud detection data science enables providers to detect anomalous activities long before a human being would notice.

The Pay-How-You-Drive (PHYD) auto insurance model uses telematics devices to collect and transmit real-time driving data to insurance companies, which informs risk assessment and pricing, enabling an optimized usage-based insurance model. Similarly, property insurance providers use telematics, such as sensors for moisture and occupancy, utility and appliance usage records, and security cameras, to create usage-based home insurance. This data can also be used to predict disasters before they happen. Life and health insurance providers can create individual “well-being” scores using data from exercise machines, social media posts about personal health, transactional data for health-related purchases, and even body sensors.

Data Science in Manufacturing

Data science is a key player in streamlining and optimizing the manufacturing industry top-to-bottom. The most common uses are predictive and preventive maintenance, market price data analysis, demand forecasting, and warranty data analysis. 

Data science drives predictive maintenance software, which enables data scientists to detect equipment anomalies and patterns, predict failures before they occur, and schedule corrective maintenance before the point of failure. This process involves data acquisition and storage, data transformation, condition monitoring, asset health evaluation, prognostics, decision support system, and a human interface layer. Examples of testing methods for equipment involved in oil and gas data science include: acoustic, corona detection, infrared, oil analysis, sound level measurements, vibration analysis, and thermal imaging predictive maintenance.

Speaking of predictions, data scientists can use machine learning methods like decision tree analysis and deep learning to predict market pricing and forecast demand. This is also implemented in the retail industry.

Data Science in Retail

At the heart of retail is supply and demand, and data science in the retail industry is used to predict just that. And with Internet stores being just as popular as brick-and-mortar stores these days, data science in e-commerce is a major component of retail. The most common data science approach in retail are time series models. The major components in retail that time series models analyze are: trends, seasonality, irregularity, and cyclicity. 

The most applicable time series models include: auto-regressive integrated moving average models, which generate results related to results for demand, sales, planning, and production; seasonal autoregressive integrated moving average models, which involve backshifts of the seasonal period; and exponential smoothing models, which create forecasts by using weighted averages of past observations to predict new values.

Data science use cases in retail include: fraud detection, price optimization, personalized marketing, inventory management, customer sentiment data analysis, intelligent cross-selling and upselling, foretelling trends through social media, customer lifetime value prediction, managing real estate, powering augmented reality, and recommendation engines.

Data Science in Supply Chain

The global supply chain is made up of a complex array of movie parts, all of which are intertwined and need to align in order for things to run smoothly. The data generated in each area of the supply chain is vast and varied. These areas include Purchasing, Manufacturing, Inventory Management, Demand Planning, Warehousing, Transportation, and Customer Service. 

Data science for logistics is a major component of supply chain management. The massive amount of data being collected by companies involved in supply chain logistics data science is only growing with the increased use of IoT devices, and only companies that know how to use data science tools to leverage this data can fully take advantage of the information that it represents. Some data science use cases in logistics include: finding efficiencies in shipping fleet management, reducing freight costs through delivery path optimization, dynamic price matching of supply to demand, warehouse optimization, forecasting demand, and estimating total delivery times.

Applications of data science in supply chain include: demand analytics, network planning, finished inventory optimization, replenishment planning analytics, procurement analytics, and transportation analytics for public sector fleet management. Data science and machine learning algorithms improve accuracy, performance, management, cost saving, pattern recognition, production management, and sales of new products.

Data Science in Energy

Data science in the energy industry has become indispensable, especially data science for renewable energy. The energy and natural resources sector is constantly evolving, presenting new opportunities and challenges every day. Data science tools and smart technologies are playing a major role in addressing challenges like environmental protection and the application of renewable energy sources. Energy and data science combine to make the most efficient use of resources, control energy flows, regulate the grid, optimize workflows, and avoid potentially costly errors. 

Renewable energy and data science intersect in areas such as solar and wind power, which rely heavily on weather prediction. Some data science energy sector use cases include: assessing the profitability of land for wind, solar, biomass, hydroelectric, or geothermal energy investments; brokerage of oil and gas acquisition and divestment; determine productivity drivers and better understand benchmark performance; oil and gas fleet management; ingest data usage patterns in order to improve solar power technology reliability; pinpoint oil reserves, and much more. Data science in the oil and gas industry will continue to be important as populations increase and green energy still has yet to gain prominence.

Data Science in Utilities

Smart sensors, smart meters, and IoT technologies generate billions of data points streaming between utility assets and edge users. This utility data contains useful information related to asset performance and resource use. Data science in utilities industry enables utility management professionals to perform real-time energy demand forecasting and avoid expensive spot market pricing; detect consumption anomalies that indicate fraud; use predictive analytics to schedule preventative maintenance and ensure assets are performing at peak efficiency; forecast weather and map out vegetation in order to to mitigate the threat of catastrophic wildfires while keeping the power grid operational, and much more. 

Some data science in utilities use cases include: failure probability modeling, analysis and balance of the energy grid, demand prediction and customer usage tracking with smart meter analysis, outage detection and prediction, utility fleet management with vehicle telematics data, dynamic energy management, mitigate utility fleet costs by analyzing driver behavior, smart grid security and theft detection, preventative equipment maintenance, demand response management, real-time customer billing, optimizing asset performance, improving operational efficiency, and enhancing customer experience.

Data Science in Agriculture

Agriculture may seem on the surface like an unlikely sphere in which to apply data science, but the modern farmer is leveraging data science applications to take full advantage of the full extent of their data, which is collected from every aspect of their production. One of the most pressing challenges in the agriculture industry is ensuring that production keeps pace with the global population. With limited natural resources available to us, data-driven decisions and sustainability are absolutely essential. 

Data science applications for agriculture include neural networks, which can detect crop disease and deploy IoT-connected sensors to monitor soil health. One such initiative is MyCrop, which is an intelligent, self-learning real-time system that identifies each farmer’s location, weather, and crop data, and uses big data, machine learning algorithms, and smartphone technology to deliver information, expertise, and resources that help smallholder farmers operate more efficiently.

Remote sensing is also an important tool in agriculture, enabling farmers to monitor and manage irrigation and soil moisture for effective crop production. Using big data on weather patterns, the US Department of Agriculture and Farm Service Agency can determine where and when destructive soil erosion and soil salinization may take place.

Data Science in Real Estate

Data science tools are helping real estate professionals and investors identify and interpret patterns in data related to real estate. These patterns can help real estate professionals better understand  real estate performance, risks, and opportunities.

Data science applications in real estate include:

  • Automated Valuation Models: uses data to generate an estimate of a property’s market value and help assess a fair transaction price for a deal
  • Property Price Indices: individual characteristics of each property can be separately priced to control for differences across assets
  • Time Series Forecasting: Uses data related to price indices, macroeconomic series, unemployment, inflation, financial indicators, stock market indices, and FX rates to forecast the path of GDP, inflation, interest rates, and more.
  • Cluster Analysis: identifies patterns to help determine which groups of properties are likely to perform similarly and which group are likely to deviate
  • Geographic Information Systems: GIS tools help us visualize, analyze, and comprehend locality intelligence, which is an extremely important factor in real estate analysis.

Data Science in Automotive Industry

Automotive data science plays a role in every step of the automotive life cycle. And with the increasing popularity of autonomous vehicles, data science in the automotive industry is only expected to grow in importance. 

Use cases for data science in the automotive industry include: 

  • Product Development: involves tasks such as analyzing new model configurations and modeling component part reliability
  • Manufacturing: involves analyzing the financial performance of suppliers in order to predict their ability to deliver on deadline
  • Autonomous Vehicles: deep learning models and sensor fusion algorithms translate IoT indicators into actionable insights - data science in IoT is critical to self-driving cars 
  • Sustainability Initiatives: helps manufacturers optimize fuel efficiency in order to meet government-set targets and company goals simultaneously

Data Science in Geotechnical Engineering

Geotechnical engineering, the branch of civil engineering concerned with the engineering behavior of earth materials, relies on data science to process and detect patterns in massive geodata sets. Whether it’s designing buildings, tunnels, bridges, or retaining structures, geotechnical engineers need to be able to easily access and efficiently process a wealth of earth material data so that they can then create predictive models. If earth material data is to be useful for geotechnical engineering purposes, it must be current, real-time, and constantly streaming. 

For example, regression machine learning models can be used in predicting blowcounts at a build site location by studying earth material behavior at the site using data such as: pile installation records, pile structural dimensions, and blowcount and hammer energy recorded during installation.

Data Science in Urban Planning

With the proliferation of IoT and smart devices, urban development planners have access to an enormous amount of data regarding the usage of city establishments, public transport, and urban living arrangements. Data science tools and geospatial intelligence can help transform this raw data into useful information and give urban planners crucial insight into how they can build better infrastructure. 

Urban planning leaders can have access to simulations with the use of Artificial Intelligence and predictive analytics, enabling them to see how proposed urban developments will affect the lives of residents. Urban planning leaders can also solicit input from citizens regarding factors such as public transportation and waste disposal and search that feedback for trends. 

Data science also plays a major role in the development of smart cities via edge networking, which involves an enormous volume of sensors through city infrastructure, transmitting data related to GPS vehicular data, social media data, mobile phone data, individual social network data, and more.

Data Science Use Cases in Telecom

Telecom connectivity is increasing by the minute. As such, telecom data is exploding, making accelerated analytics and data science the future of the telecom industry. Data related to cabling structure maps, number of network devices, customer usage data, hardware maintenance schedules, network vulnerabilities, competitor pricing and more are all involved in the telecom network design, planning, and optimization process. 

Some data science use cases in telecom include: 

  • Product Optimization: sentiment analysis gathers customer feedback on products
  • Increased Network Security: automated network anomaly detection exposes vulnerabilities in real-time
  • Predictive Analytics: continuously streaming device data drives predictive models
  • Network Reliability Analysis: identify issues that harm network reliability by querying and visualizing the entire stream of telco data at advanced speeds 
  • Fraud Detection: data science in fraud detection helps quickly detect anomalous activities, such as unauthorized access, fake profiles, and misuse of credit card information.
  • Price Optimization: customer segment demographic, competitor pricing, and customer churn data science are used to set optimal pricing
  • Real-Time Analytics: real-time data related to network, traffic, customers, and more helps providers understand how users feel about the service/product
  • Customer Churn Analysis: analyzing customer behavior, feedback, social posts, etc. is key to correcting course and predicting churn in data science
  • Targeted Marketing: recommendation engines help predict what customers may want in the future based on their usage of different services
  • CLV Prediction: data related to frequency, recency, and total amount of purchases by each customer is plugged into a CLV model 
  • Location-Based Promotion: real-time geolocation detection powers location-based promotions
  • Mobile Data Offloading: the use of Wi-Fi networks to offload cellular traffic in order to reduce congestion and save money

The HEAVY.AI Advantage

We’ve gone over nearly all of the major data science case studies examples. So what does HEAVY.AI bring to the table for the myriad data science use cases pervading our everyday lives? HEAVY.AI’s hardware accelerated analytics delivers greater speed and performance than any legacy system could handle. Instant interactivity coupled with dynamic visualizations of big data drives faster action and better decisions. 

All of these advantages mean decreased time to value, an improved ability to: ask the right questions and define business objectives, fix inconsistencies and gaps in data, analyze data and form hypothesis about problems, conduct effective feature engineering, perform fast data discovery, train accurate predictive models, monitor ML models, and create interactive data visualizations that reveal hidden insights and clearly communicate findings to key stakeholders – all at previously unheard-of speeds. 

When HEAVY.AI gives data scientists the power to explore big data at the speed of thought, the questions you can answer are limitless. 

If you enjoyed learning about these various data science examples, check out our comprehensive list of big data analytics examples next!