Students build foundational programming skills in Python along with core data science concepts — data types, data wrangling with pandas, exploratory data analysis (EDA), and basic statistical reasoning — forming the launch pad for the entire minor.

Students transform raw datasets into meaningful visual narratives using tools such as Matplotlib, Seaborn, and Tableau. They learn descriptive analytics, dashboarding techniques, and storytelling with data for informed business decisions.

Students implement supervised and unsupervised ML algorithms — linear regression, decision trees, clustering, and model evaluation — using scikit-learn. They select appropriate models, tune hyperparameters, and interpret results critically.

Students gain hands-on experience with Hadoop, Spark, and the R ecosystem for statistical computing. They process large-scale datasets, run distributed workflows, and apply advanced statistical models for predictive analytics.

Students apply data science methodologies to cybersecurity — anomaly detection, log analysis, intrusion pattern recognition, and threat intelligence pipelines — bridging data expertise with real-world security operations.