Data 100: Principles and Techniques of Data Science

UC Berkeley, Spring 2026

jdgg AT berkeley DOT edu

ramesh_s AT berkeley DOT edu

Feb 9 · 1 min read

Tue, Jan 20	Lecture 1 Introduction	Reading Note 1
Wed, Jan 21	Lab ReleasedLab 1: Prerequisite Coding Homework ReleasedHomework 1: Prerequisite Math Homework ReleasedHomework 1: Coding
Thu, Jan 22	Lecture 2 Pandas Part I	Reading Note 2
Fri, Jan 23

Tue, Jan 27	Lecture 3 Pandas Part II Lab Due Lab 1: Prerequisite Coding	Reading Note 3
Wed, Jan 28	Discussion 1Prerequisites
Thu, Jan 29	Lecture 4 Pandas Part III Lab Released Lab 2A: Pandas Homework ReleasedHomework 2A: Food Safety I	Reading Note 4
Fri, Jan 30	Catch-Up 1Pandas Homework DueHomework 1: Prerequisite Math

Mon, Feb 02	Exam PrepExam Prep 1: Pandas	Solutions Walkthrough
Tue, Feb 03	Lecture 5 Data Wrangling and EDA Lab Due Lab 2A: Pandas	Reading Note 5
Wed, Feb 04	Discussion 2Pandas I
Thu, Feb 05	Lecture 6 Regex Lab Released Lab 2B: Data Cleaning, EDA Homework ReleasedHomework 2B: Food Safety II	Reading Note 6
Fri, Feb 06	Catch-Up 2Data Cleaning, EDA, Regex Homework DueHomework 2A: Food Safety I

Mon, Feb 09	Exam PrepExam Prep 2: Pandas and Regex
Tue, Feb 10	Lecture 7 Visualization I Lab Due Lab 2B: Data Cleaning, EDA
Wed, Feb 11	Discussion 3Pandas II, EDA
Thu, Feb 12	Lecture 8 Visualization II Lab Released Lab 3: Regex, EDA Homework ReleasedHomework 3: NYT Articles
Fri, Feb 13	Catch-Up 3Visualizations Homework DueHomework 2B: Food Safety II

Tue, Feb 17	Lecture 9 Sampling
Wed, Feb 18	Discussion 4Regex, Visualization, and Transformation
Thu, Feb 19	Lecture 10 Modeling, SLR Lab Released Lab 4: Tranformations Homework ReleasedHomework 4: Bike Sharing
Fri, Feb 20	Catch-Up 4Sampling, Modeling, SLR

Tue, Feb 24	Lecture 11 Constant Model, Loss, Transformations
Wed, Feb 25	Discussion 5Probability, Sampling, and Visualization
Thu, Feb 26	Lecture 12 Ordinary Least Squares I Lab Released Lab 5: Modeling, Summary Statistics, Loss Functions, and OLS Homework ReleasedHomework 5: Modeling and OLS
Fri, Feb 27	Catch-Up 5Constant Model, Loss, Transformations

Tue, Mar 03	Lecture 13 Ordinary Least Squares II, Gradient Descent I
Wed, Mar 04	Discussion 6Modeling and OLS
Thu, Mar 05	Lecture 14 Gradient Descent II
Fri, Mar 06	Exam ReviewMidterm 1 Review

Mon, Mar 09	ExamMidterm 1 (CBTF, Monday-Thursday)
Tue, Mar 10	Lecture 15 Feature Engineering
Wed, Mar 11	Discussion 7Gradient Descent
Thu, Mar 12	Lecture 16 Case Study HCE: CCAO Lab Released Lab 6: Gradient Descent, sklearn Project A1Housing I
Fri, Mar 13

Tue, Mar 17	Lecture 17 Cross-Validation, Regularization
Wed, Mar 18	Discussion 8Feature Engineering, Housing
Thu, Mar 19	Lecture 18 Random Variables Lab Released Lab 7: Model Selection Project A2Housing II
Fri, Mar 20	Catch-Up 6Feature Engineering, CV, Regularization, RVs

Tue, Mar 31	Lecture 19 Estimators, Bias, and Variance
Wed, Apr 01	Discussion 9Cross-Validation and Regularization
Thu, Apr 02	Lecture 20 Parameter Inference & Bootstrap Lab Released Lab 8: Probability Homework ReleasedHomework 6: Probability
Fri, Apr 03	Exam ReviewMidterm 2 Review

Mon, Apr 06	ExamMidterm 2 (CBTF, Monday-Thursday)
Tue, Apr 07	Lecture 21 SQL I
Wed, Apr 08	Discussion 10RVs, Bias, and Variance
Thu, Apr 09	Lecture 22 SQL II Lab Released Lab 9: SQL Homework ReleasedHomework 7: SQL
Fri, Apr 10

Tue, Apr 14	Lecture 23 Logistic Regression I
Wed, Apr 15	Discussion 11SQL
Thu, Apr 16	Lecture 24 Logistic Regression II Lab Released Lab 10: Logistic Regression Project B1Spam & Ham I
Fri, Apr 17	Catch-Up 7SQL, Logistic Regression

Tue, Apr 21	Lecture 25 PCA and Clustering I
Wed, Apr 22	Discussion 12Logistic Regression
Thu, Apr 23	Lecture 26 PCA and Clustering II Lab Released Lab 11: PCA and Clustering Project B2Spam & Ham II
Fri, Apr 24	Exam ReviewMidterm 3 Review

Mon, Apr 27	DiscussionPCA and Clustering (Recorded)
Tue, Apr 28	Lecture 27 Guest Lecture
Wed, Apr 29
Thu, Apr 30	ExamMidterm 3 (Paper)
Fri, May 01