A hands-on introduction to the methods of data science. Strategies for analyzing and visualizing tabular data, including common patterns and pitfalls. Data acquisition through web scraping and REST APIs. Core principles of machine learning: supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. Introduction to data of different shapes and sizes, including text, image, and geospatial data. The focus is on intuition and implementation, rather than theory and math. Implementation is in Python and Jupyter notebooks, using libraries such as pandas and scikit-learn. Course culminates in a final project where students apply the methods to a data science problem of their choice. Prerequisite: CS 106A or equivalent programming experience in Python. (Students with experience in another programming language should take CS 193Q to catch up on Python.)
5 units · Letter (ABCD/NP) · GER: WAY-AQR
A hands-on introduction to the methods of data science. Strategies for analyzing and visualizing tabular data, including common patterns and pitfalls. Data acquisition through web scraping and REST APIs. Core principles of machine learning: supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. Introduction to data of different shapes and sizes, including text, image, and geospatial data. The focus is on intuition and implementation, rather than theory and math. Implementation is in Python and Jupyter notebooks, using libraries such as pandas and scikit-learn. Course culminates in a final project where students apply the methods to a data science problem of their choice. Prerequisite: CS 106A or equivalent programming experience in Python. (Students with experience in another programming language should take CS 193Q to catch up on Python.)
Offered in Winter 2027, Spring 2027 at Stanford University.