Principles and Techniques of Data Science

UC Berkeley, Summer 2021

Please read our course FAQ before contacting staff with questions that might be answered there.
The Syllabus contains a detailed explanation of how each course component will work this summer, given that the course is being taught entirely online.
The scheduling of all weekly events is in the Calendar.
The Zoom links for all live events are in @6 on Piazza.
Note:The schedule of lectures and assignments is subject to change.

Jun 21: Lecture 1 Introduction, Course Overview; Homework 1 Prerequisites (due Jun 24); Lab 1 Prerequisite Coding (due Jun 26); Lab 2 Pandas (due Jun 26)
Jun 22: Lecture 2 Data Sampling and Probability
Jun 23: Lecture 3 Estimators and Bias; Discussion 1 Prerequisites, Probability (solutions)
Jun 24: Lecture 4 Pandas I; Homework 2 Trump Sampling (due Jun 29)
Jun 25: Live Session 1 (recording)

Jun 28: Lecture 5 Pandas II; Homework 3 Food Safety (due Jul 5); Lab 3 Data Cleaning and EDA (due Jul 3); Lab 4 SQL (due Jul 3); Discussion 2 Probability, Pandas (videos) (solutions)
Jun 29: Lecture 6 Data Cleaning and EDA
Jun 30: Lecture 7 Regular Expressions; Discussion 3 Pandas (code) (solutions)
Jul 1: Lecture 8 SQL
Jul 2: Live Session 2 (recording)

Jul 5: Independence Day; Homework 4 Tweets (due Jul 8); Lab 5 Transformations and KDEs (due Jul 10); Lab 6 Modeling, Summary Statistics, and Loss Functions (due Jul 10); Discussion 4 SQL, Regex (videos) (solutions)
Jul 6: Lecture 9 Visualization I
Jul 7: Lecture 10 Visualization II; Discussion 5 Visualization (solutions)
Jul 8: Lecture 11 Modeling; Homework 5 Bike Sharing (due Jul 12)
Jul 9: Live Session 3 (recording) (code)

Jul 12: Lecture 12 Simple Linear Regression; Homework 6 Regression (due Jul 19); Lab 7 Simple Linear Regression (due Jul 17); Discussion 6 Modeling and Simple Linear Regression (solutions)
Jul 13: Lecture 13 Ordinary Least Squares + Geometric Interpretation
Jul 14: Lecture 14 Feature Engineering; Discussion 7 Midterm Review
Jul 15: Midterm Midterm Exam (9:30 AM - 11:00 AM) (blank) (solutions) (video)
Jul 16: Live Session 4 (recording)

Jul 19: Lecture 15 Modeling in Context: Fairness in Housing Appraisal; Homework 7 Housing I (due Jul 22); Lab 8 Multiple Linear Regression and Feature Engineering (due Jul 24); Lab 9 Feature Engineering and Cross-Validation (due Jul 24); Discussion 8 Multiple Linear Regression (solutions)
Jul 20: Lecture 16 Bias and Variance
Jul 21: Lecture 17 Cross-Validation + Regularization; Discussion 9 Bias-Variance, HCE (solutions)
Jul 22: Lecture 18 Gradient Descent; Homework 8 Housing II (due Jul 26)
Jul 23: Live Session 5 (video pt. 1) (code)

Jul 26: Lecture 19 Logistic Regression I; Homework 9 Gradient Descent (due Jul 29); Lab 10 Logistic Regression (due Jul 31); Lab 11 Decision Trees (due Jul 31); Discussion 10 Regularization, Cross-Validation, Gradient Descent (solutions)
Jul 27: Lecture 20 Logistic Regression II and Classification
Jul 28: Lecture 21 Decision Trees; Discussion 11 Logistic Regression, Classification (solutions)
Jul 29: Lecture 22 Inference for Modeling; Homework 10 Spam/Ham I(due Aug 2)
Jul 30: Live Session 6 (video) (code)

Aug 2: Lecture 23 Principal Component Analysis; Homework 11 Spam/Ham II (due Aug 5); Lab 12 Principal Component Analysis (due Aug 7); Lab 13 Clustering (due Aug 7); Discussion 12 Decision Trees, Inference (solutions)
Aug 3: Lecture 24 Clustering I
Aug 4: Lecture 25 Clustering II; Discussion 13 Principal Component Analysis, Clustering (solutions)
Aug 5: Lecture 26 Conclusion (video) (slides); Homework 12 Principal Component Analysis (due Aug 9)
Aug 6: Live Session 8 NA

Data 100