CSCI 9 · Practical Data Science

Lecture and lab notebooks for the course. Open any linked notebook in your browser—no installation required.

Browse materials Course overview

Course textbook

How to use this page: Use Lecture for slide decks and Textbook for reading. Use Demo and Lab to open course materials in your browser—no installation required. Your instructor’s syllabus is the final word on what to submit and when.

Course schedule
Week Title Lecture Demo Textbook Lab
1 Intro / CSCI-8 Review Lecture intro intro Lab 01: Data Types and Arrays
2 Pandas I Lecture pandas_1 pandas-i Lab 02: Jupyter, NumPy, and Matplotlib
3 Pandas II Lecture pandas_2 pandas-ii —
4 Pandas III Lecture pandas_3 pandas-iii Lab 03A: Pandas Overview I
5 Exploratory Data Analysis Lecture — eda Lab 03B: Pandas Overview II
5 Text Wrangling and Regex Lecture text_wrangling_regex text-wrangling-regex Lab 04: EDA, Regex
6 Visualization I Lecture visualization_1 visualization-i —
7 Visualization II Lecture visualization_2 visualization-ii Lab 06: CalEnviroScreen — Environmental Justice
8 Guest Speaker — — — —
9 Sampling Lecture 1 Lecture 2 — sampling Lab 05: Visualization, Transformations, and KDEs
10 Modeling I: Simple Linear Regression Lecture modeling_1 modeling-i —
11 Modeling II: Feature Engineering Lecture modeling_2 modeling-ii Lab 07: Linear Regression Modeling and Sklearn
12 Modeling III: Standardization, Multicollinearity, and Generalization — modeling_3 modeling-iii —
13 Modeling III: Hyperparameters and Cross-Validation — modeling_3 modeling-iii —
14 Small Group Presentations — — — —
15 Modeling IV: Decision Trees and Random Forests — modeling_4 modeling-iv Lab 08: Stanford Open Policing Project
16 SQL I Lecture — sql-i —
16 SQL II Lecture — sql-ii —