Functional Data Processing and Tools

This unit synthesizes functional programming practices and practical tooling to prepare, parse, store, model, and share real-world data using Haskell and Python ecosystems. Students apply binary/text handling, parsing, database integration, and scientific/machine learning libraries while documenting and packaging reproducible workflows for collaborative contexts.

Learning Objectives

• Analyze and transform binary and textual datasets using Haskell (bytestring, text, aeson, attoparsec) and Python (bytes, stringsearch, text) libraries to produce correctly encoded, validated inputs for downstream processing.
• Apply parsing, serialization, and persistence tools (attoparsec, aeson, SQLAlchemy) to parse and validate structured data, serialize to JSON or binary formats, and persist results to relational stores with verifiable integrity.
• Demonstrate the ability to prototype and evaluate data science and machine learning solutions using SciPy, XGBoost, Cloudera Oryx, and deep learning tools; integrate model experiments into reproducible notebooks and measure performance with standard metrics.
• Evaluate and document code and team workflows for reproducibility and collaboration by using language conventions (indentation, comments), help resources, developer utilities (PrettyTable, Toolz, funcy, moo), and notebook lifecycle practices in applied domains such as health care.