Advanced Data Handling and Tools

This unit advances students’ abilities to process, parse, and serialize binary and textual data in both Haskell and Python, and introduces practical libraries and tools for data-driven game programming and design. Students will integrate parsing libraries, data-processing and machine-learning toolchains, and development best practices to build, document, and evaluate robust pipelines and components ready for deployment or further specialization.

Learning Objectives

• Implement robust binary and text input/output and parsing routines in Haskell and Python using libraries such as bytestring, text, attoparsec, and equivalent Python tools to correctly read, validate, and write game-related data files
• Apply JSON serialization and deserialization techniques with aeson (Haskell) and comparable Python libraries to exchange structured data between game modules and external systems
• Design and implement end-to-end data workflows and prototypes that leverage tooling (PrettyTable, Jupyter Notebook, SQLAlchemy) and machine learning libraries (SciPy, XGBoost, Cloudera Oryx) to process, train, and evaluate models for game or domain-specific features
• Evaluate code quality, performance, and maintainability by using language conventions (indentation, comments), functional utility libraries (toolz, funcy, moo), and low-level optimization approaches, and prepare artifacts and documentation to support collaboration and deployment