Data 100: Principles and Techniques of Data Science
UC Berkeley, Summer 2024
Ed Datahub Gradescope Lectures Playlist Emergency Accommodations Office Hours Queue
Schedule
Week 1
 June 17

 Lecture 1 Course Overview
 Note 1
 Lab 1 Prerequisite Coding, Plotting, and Permutation (due 6/20)
 June 18
 Homework 1A Plotting and Permutation Tests (due 6/20)
 Homework 1B Prerequisite Math (due 6/20)

 Discussion 1 Prerequisites (virtual walkthrough only)
 Solutions
 June 19
 Juneteenth
 June 20
 Lab 2 Pandas (due 6/23)
 June 21

 Lecture 4 Pandas III
 Note 4
 Homework 2 Food Safety I (due 6/24)
Week 2
 June 24

 Lecture 5 Data Wrangling and EDA I
 Note 5
 Lab 3 Data Wrangling and EDA (due 6/26)
 June 25

 Lecture 6 Text Wrangling and Regex
 Note 6
 Homework 3 Food Safety II (due 6/27)
 June 26

 Discussion 3 Regex and EDA
 Solutions
 June 27

 Lecture 7 Visualization I
 Note 7
 Lab 4 Regex and EDA (due 6/30)
 June 28

 Lecture 8 Visualization II (Guest: Jun Yuan)
 Note 8
 Homework 4 Text Analysis of Bloomberg Articles (due 7/1)
Week 3
 July 1
 Lab 5 Transformations (due 7/3)

 Discussion 4 Visualization and Transformation
 Solutions
 July 2

 Lecture 10 Modeling and SLR
 Note 10
 Homework 5 Bike Sharing (due 7/4)
 July 3
 No Discussion
 July 4
 No Lecture
 Homework 6A Sampling (due 7/8)
 Homework 6B Modeling (due 7/8)
 July 5
 No Lecture
Week 4
 July 8

 Lecture 11 Constant Model, Loss, and Transformations
 Note 11
 Lab 6 Modeling, Loss Functions, and Summary Statistics (due 7/10)
 July 9

 Lecture 12 OLS (Multiple Regression)
 Note 12
 Homework 7 Regression (due 7/11)
 July 10
 July 11

 Lecture 13 Gradient Descent and sklearn
 Note 13
 Lab 7 Ordinary Least Squares (due 7/14)
 July 12

 Lecture 14 Feature Engineering
 Note 14
 Project A1 Housing I (due 7/16)
Week 5
 July 15
 Lab 8 Gradient Descent and sklearn (due 7/17)

 Discussion 7 Gradient Descent and Feature Engineering
 Solutions
 July 16

 Lecture 16 HCE Case Study: CCAO (Prerecorded)
 Note 16
 Project A2 Housing II (due 7/22)
 July 17

 Discussion 8 Exam Review
 Solutions
 July 18

 Lecture 17 Random Variables
 Note 17
 Lab 9 Model Selection, Regularization, and CrossValidation (due 7/21)
 July 19
 Midterm Midterm
Week 6
 July 22

 Lecture 18 Estimators, Bias, and Variance
 Note 18
 Lab 10 Probability (due 7/24)

 Discussion 9 CrossValidation and Regularization
 Solutions
 July 23

 Lecture 19 Parameter Inference and Bootstrapping
 Note 19
 Homework 8 Probability and Estimators (due 7/25)
 July 24

 Discussion 10 Random Variables, Bias, and Variance
 Solutions
 July 25
 Lab 11 SQL (due 7/28)
 July 26

 Lecture 21 Logistic Regression I
 Note 22
 Homework 9 SQL (due 7/29)
Week 7
 July 29

 Lecture 22 Logistic Regression II
 Note 23
 Lab 12 Logistic Regression (due 7/31)
 Project B1 Spam and Ham I (due 8/1)

 Discussion 11 SQL (Worksheet Notebook)
 Solutions
 July 30

 Lecture 23 Decision Trees
 Note 27
 July 31

 Discussion 12 Logistic Regression
 Solutions
 August 1
 Lab 13 PCA (due 8/4)
 Project B2 Spam and Ham II (due 8/5)
 August 2

 Lecture 25 Clustering
 Note 26
 Homework 10 PCA and Decision Trees (Extra Credit) (due 8/5)
Week 8
 August 5
 Lecture 26 Intro to Diffusion Models & Conclusion
 Lab 14 Clustering (due 8/7)

 Discussion 13 PCA and Clustering
 Solutions
 August 6
 Lecture 27 Industry/Academia Roundtable Discussion: Pioneering Pathways: Data Science, AI and Career
 August 7
 Discussion 14 Final Review
 August 8
 Final Exam Final