Data Science 207

Applied Machine Learning

3 units

Course Description

Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important.

Skill Sets

Experimental design / Working with machine learning algorithms / Feature engineering / Prediction vs. explanation / Network analysis / Collaborative filtering


Python / Python libraries for linear algebra, plotting, machine learning: NumPy, Matplotlib, sk-learn / GitHub for submitting project code

Course Designers

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Josh Blumenstock
Joshua Blumenstock
Associate Professor Alumni (PhD 2012)
207C South Hall

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Daniel Gillick
Assistant Adjunct Professor

Previously listed as DATASCI W207.

Course must be taken for a letter grade to fulfill degree requirements.


Data Science 201 and W03. Intermediate competency in Python, C, or Java, and competency in Linux, GitHub, and relevant Python libraries; or permission of instructor. Linear algebra is recommended. MIDS students only.

Requirements Satisfied

Applied Data Science Certificate — Analytical Methods and Techniques of Data Science

Last updated:

October 6, 2022