Advanced Data Integration and Analysis

This capstone unit covers advanced techniques for acquiring, cleaning, transforming, visualizing, and sharing data using Python tools. Students will work with diverse sources (NoSQL, web APIs, graphs, images), explore basic neural network prototypes, and apply reproducible workflows and documentation to prepare for further study or workplace projects.

Learning Objectives

• Analyze heterogeneous datasets to detect and handle missing or inconsistent values and perform concatenation, filtering, aggregation, and distribution transformations using Python data libraries
• Apply data acquisition methods to connect to and extract data from NoSQL databases, web APIs, large graph datasets, and file-based sources (including images and handwritten data), and integrate them into analysis workflows
• Demonstrate creation and interpretation of visualizations and statistical measures (network graphs, correlation analyses, Cramér’s V) to reveal relationships and support data-driven conclusions
• Evaluate reproducibility and collaboration practices by documenting data lineage, sharing and versioning notebooks, and iterating on simple neural network prototypes while leveraging curated community and developer resources