Combining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. Modeled on Berkely’s Data 100 course, this course introduces basic concepts in data science.
In this class, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Through a strong emphasizes on data centric computing, quantitative critical thinking, and exploratory data analysis this class covers key principles and techniques of data science. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.

Schedule

WEEK 1 - Welcome to M287, pandas and python for Data Science.
WEEK 2 - Sampling WEEK 3 - Exploratory Data Analysis WEEK 4 - Vizualization WEEK 5 - Linear Regression WEEK 6 - Feature Engineer
WEEK 7 - Lab Week: The Data Cycle
WEEK 8 - Regularization and Cyber secutrity (Invited Talk by Prof. Jeremiah Onaolapo) WEEK 9 - Classification WEEK 10 - Decision Trees WEEK 11 - Gradient Boosting
WEEK 12 - Clustering
WEEK 13 - THANKSGIVING WEEK 14 - Neural Networks WEEK 15 - Data Ethics (Invited Talk by Juniper Lovato) and Semester review

Textbook

Principals and Techniques of Data Science

Syllabus

Fall 2022 Syllabus