Foundations of Python Data Science

This unit establishes the practical environment and foundational skills for data science using Python. Students will set up and use interactive environments (Anaconda, Jupyter, Google Colab), ingest and prepare tabular and textual data, perform exploratory data analysis and visualization, and apply introductory machine learning and privacy-aware prototyping techniques.

Learning Objectives

• Demonstrate installation and configuration of Python environments and tools (Anaconda, Jupyter Notebook, Google Colab) and create notebooks that integrate code, multimedia, and documentation
• Apply data ingestion and preparation techniques to upload, stream, sample, and access structured flat-file and text data using NumPy and pandas
• Analyze datasets through exploratory data analysis and visualization by selecting appropriate graph types, performing transformations (including nonlinear), detecting outliers, and summarizing key patterns
• Evaluate and implement basic machine learning workflows (tokenization with bag-of-words, SVD, k-means clustering, linear regression, decision trees) to perform rapid prototyping while applying methods to identify and remove personally identifiable information