Healthcare domain:

Predicting hospital readmissions: Develop a machine learning model to predict the likelihood of patients being readmitted to the hospital within 30 days after being discharged. The model can be trained on historical patient data that includes diagnoses, procedures, demographics, lab tests, medications, length of stay etc. This can help hospitals focus their care management resources on high-risk patients.

Improving disease diagnosis: Build a deep learning model to analyze medical imaging data like CT/MRI scans to detect diseases like cancer, tumors etc. The model can be trained on a large dataset of labeled medical images. This has potential to make disease diagnosis more accurate and faster.

Monitoring public health with nontraditional data: Use alternative data sources like search engine queries, social media posts, smartphone data to build indicators for tracking and predicting things like flu outbreaks, spread of infectious diseases. The insights can help public health organizations develop early detection systems.

Retail and e-commerce domain:

Predicting customer churn: Develop machine learning classifiers to identify customers who are likely to stop using or purchasing from a company within the next 6-12 months based on their past behavior patterns, demographics, purchase amount/frequency etc. This helps companies prioritize customer retention efforts.

Improving demand forecasting: Build deep learning models using time series data to more accurately forecast demand for products over different time horizons (weekly, monthly, quarterly etc). The models can be trained on historic sales data, events, seasonality patterns, price fluctuations etc. This helps effective inventory planning and supply chain management.

Optimizing product recommendations: Create recommendation systems using collaborative filtering techniques to suggest additional relevant products to customers during and after purchases based on their preferences, past purchase history and behavior of similar customers. This can boost cross-sell and up-sell.

Finance and banking domain:

Credit risk modeling: Develop machine learning based credit scoring models to assess risk involved in giving loans to potential customers using application details and past transaction history. the models are trained on performance data of existing customers to identify attributes that can predict future defaults.

Investment portfolio optimization: Build algorithms that can suggest optimal asset allocation across different classes like stocks, bonds, commodities etc based on an investor’s goals, risk profile and market conditions. Advanced optimization techniques are used along with historic market performance data.

Fraud detection: Create neural networks that can detect fraudulent transactions in real-time by analyzing spending patterns, locations, device details etc. The models learn typical customer behavior from historical transaction logs to identify anomalies. This helps reduce financial losses from fraud.

Transportation domain:

Predicting traffic flow: Develop deep learning models that can forecast traffic conditions on roads, highways and critical intersections/areas during different times of day or events based on historical traffic data, schedules, road incidents etc. The insights enable better urban planning and routing optimizations.

Optimizing public transit systems: Build simulations and recommendation systems to analyze ridership data and suggest most cost-effective routes, bus/metro scheduling, station locations that minimize passenger wait times. The goal is to improve transit system efficiency using optimization techniques.

Reducing emissions from logistics: Create algorithms that combine vehicle data with maps/navigation to plot low-carbon routes for fleet vehicles used in delivery, hauling etc. Advanced planning helps reduce fuel costs as well as carbon footprint of transportation sector.

The above represent some examples of how data science is being applied to solve critical challenges across industries. In each case, the focus is on leveraging historical and streaming data sources through techniques like machine learning, deep learning, optimization, simulations etc. to build predictive and prescriptive models. This drives better decision making and helps organizations optimize operations, costs as well as customer and social outcomes.