Applied Data Science Integration

This unit integrates data access, cleaning, visualization, and introductory modeling workflows using Python notebooks and real-world datasets. Students will connect to diverse data sources (NoSQL, web, images), prepare and document datasets, produce interpretable visualizations and statistics, and implement basic neural and recommender models while sharing reproducible notebooks.

Learning Objectives

• Analyze and connect to diverse data sources (NoSQL databases, web APIs, and large graph stores) and execute reproducible data-extraction code within Python notebooks
• Apply data-cleaning and transformation techniques (handling missing data, concatenation, filtering, aggregation, and documenting data lineage) to produce analysis-ready datasets
• Demonstrate creation and interpretation of visualizations and statistical measures (correlation, Cramér’s V, distribution modification) to communicate patterns and relationships
• Evaluate and implement basic predictive models and recommender workflows (simple neural networks, MovieLens recommender) and share reproducible notebooks and artifacts for collaboration