Applied Data Acquisition and Analysis

This capstone unit brings together data acquisition, cleaning, exploratory analysis, visualization, basic modeling, and reproducibility workflows using Python tools. Students will work with diverse sources — NoSQL and web APIs, large graphs, images, and tabular datasets — and prepare shared, documented notebooks that demonstrate end-to-end data science practices.

Learning Objectives

• Analyze datasets by handling missing data, performing filtering, concatenation, aggregation, transformations, and computing association measures (e.g., correlation, Cramér’s V) to support feature selection and exploratory data analysis
• Apply methods to acquire and interact with data from multiple sources including NoSQL databases, web APIs, large graph stores, image/handwritten inputs, and public datasets (e.g., MovieLens), using Python clients and notebook environments
• Demonstrate the ability to create and interpret visualizations for tabular relationships and network structures, including correlation matrices, graph visualizations, and image displays to communicate findings
• Evaluate data provenance, reproducibility, and authority by documenting data lineage, citing authoritative sources, sharing and versioning notebooks, and justifying source selection for analyses