LibGuides: CSC448: Module 4

Module 4 Required Resources

Intro to Python for Computer Science and Data Science
Deitel, P., & Deitel, H. (2019). Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud (1st ed.). Pearson Education.

Chapter 9: Focus your reading on Files, text file preprocessing, serialization with JSON, Security: pickle serialization and deserialization, Handling exception, finally clause, Explicitly raising an exception

Chapter 10: Focus your reading on controlling accessing to attribute, properties for Data access, Simulating private attribute, Inheritance: Base classes and Subclasses, Duck typing and polymorphism, Operator overloading, Exception Class Hierarchy and Custom Exceptions, Named Tuples, Intro to Data Science: Time Series and Simple Linear Regression

Module 4 Optional Resources

Practical Statistics for Data Scientist
Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical Statistics for Data Scientist (2nd ed.). Pearson Education.

Chapter 4: Focus your reading on Simple Linear Regression, Multiple Linear Regression, Prediction using Regression, Factor variables in regression, and Interpreting the regression equation.

Each of the chapters and medium articles (1) How to Perform Hypothesis Testing Using Python, (2) Multiple Linear Regression Implementation in Python , (3) The Ultimate Markdown Guide (for Jupyter Notebook) will offer you a well-rounded understanding of module 4 concepts and prepare you for week 4 activities.

Multiple Linear Regression Implementation in Python
Machine Learning with Python. (2020, February 9). Multiple Linear Regression Implementation in Python. Medium.

This beginner‑focused guide explains how to implement multiple linear regression in Python using scikit-learn, walking through data preparation, model training, coefficient interpretation, and evaluation with a real-world advertising dataset. It highlights key concepts like dataset splitting, algorithm execution, and understanding model performance metrics in a clear, step‑by‑step manner.
The Ultimate Markdown Guide (for Jupyter Notebook)
Satopay, H. (2019, November 18). The Ultimate Markdown Guide (for Jupyter Notebook). Analytics Vidhya on Medium.

This guide explains how to use Markdown cells in Jupyter Notebooks, covering essential syntax (headings, emphasis, lists, code blocks, links, images, HTML integration) to create clear, well-documented, and visually appealing notebooks. Perfect for beginners aiming to improve readability and presentation in their data science workflows.

Illumination. (2023, March). How to Perform Hypothesis Testing Using Python. Medium.
This article introduces hypothesis testing using Python, guiding readers through formulating hypotheses, running statistical tests, and interpreting results with a real-world income example. It’s a practical, beginner-friendly tutorial with step-by-step code and explanations.

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