Lecture 2: Data Science Fundamentals

(Last updated: Jan 27, 2026)

This lecture recaps the fundamentals of data science, such as table operations, classification, and regression.

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Preparation

Read the required course readings.

Lecture

Below are the slides:

Below is the link to the online notebook:

Follow the steps on the notebook page to set up the notebook.

Required Course Readings

  • The following sections in book An Introduction to Statistical Learning (James et al., 2013)
    • 2.2.1 (Measuring the Quality of Fit)
    • 3.1.1 (Estimating the Coefficients)
    • 3.1.3 (Assessing the Accuracy of the Model)
    • 9.1.1 (What Is a Hyperplane?)
    • 9.1.2 (Classification Using a Separating Hyperplane)

Optional Course Readings

  • Section 5.3 (Hyperparameters and Validation Sets, including 5.3.1) in book Deep Learning (Goodfellow et al., 2016).
  • Section 4.5.1 (Rosenblatt’s Perceptron Learning Algorithm) in book The Elements of Statistical Learning (Hastie et al., 2009)

Additional Resources

Below are website for data visualization inspirations:

Below are interesting data science case studies:

The textbook below contains more information about how to select models:

The websites below contains exercises for Python pandas:

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