Data 100: Principles and Techniques of Data Science

UC Berkeley, Summer 2024

Ed Datahub Gradescope Lectures Playlist Emergency Accommodations Office Hours Queue

Kevin Miao

He/Him/His

kevinmiao@berkeley.edu

Office Hours: Mondays & Fridays from 11:00AM to 12:00PM in Evans 455

Maya Shen

She/Her/Hers

mayashen@berkeley.edu

Office Hours: Tuesdays & Thursdays 11:00AM to 12:00PM in Evans 455

Schedule

Week 1

June 17

Lecture 1 Course Overview

Note 1

Lab 1 Prerequisite Coding, Plotting, and Permutation (due 6/20)

June 18

Lecture 2 Pandas I

Note 2

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

Lecture 3 Pandas II

Note 3

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)

Discussion 2 Pandas (Worksheet Notebook) (Groupwork Notebook)

Worksheet Solutions Groupwork Solutions

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

Lecture 9 Sampling

Note 9

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)

Discussion 5 Probability, Sampling, and Simple Linear Regression

Solutions

July 9

Lecture 12 OLS (Multiple Regression)

Note 12

Homework 7 Regression (due 7/11)

July 10

Discussion 6 Constant Models, OLS, and Multiple Linear Regression

Solutions

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

Lecture 15 Cross-Validation and Regularization (Guest: Matthew Shen)

Note 15

Lab 8 Gradient Descent and sklearn (due 7/17)

Discussion 7 Gradient Descent and Feature Engineering

July 16

Lecture 16 HCE Case Study: CCAO (Prerecorded)

Note 16

Project A2 Housing II (due 7/22)

July 17

Discussion 8 Exam Review

July 18

Lecture 17 Random Variables

Note 17

Lab 9 Model Selection, Regularization, and Cross-Validation (due 7/21)

July 19

Midterm Midterm

Week 6

July 22

Lecture 18 Estimators, Bias, and Variance

Note 18

Lab 10 Probability (due 7/10)

Discussion 9 Cross-Validation and Regularization

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

July 25

Lecture 20 SQL

Note 20

Lab 11 SQL (due 7/28)

July 26

Lecture 21 Logistic Regression I

Note 21

Homework 9 SQL (due 7/29)

Week 7

July 29

Lecture 22 Logistic Regression II

Note 22

Lab 12 Logistic Regression (due 7/31)

Project B1 Spam and Ham I (due 8/1)

Discussion 11 SQL

July 30

Lecture 23 Ensembles

July 31

Discussion 12 Logistic Regression

August 1

Lecture 24 PCA

Note 24

Lab 13 PCA (due 8/4)

August 2

Lecture 25 Clustering

Note 25

Project B2 Spam and Ham II (due 8/5)

Homework 10 PCA and Clustering (due 8/5)

Week 8

August 5

Lecture 26 Conclusion

Lab 14 Clustering (due 8/7)

Discussion 13 PCA and Clustering

August 6

Lecture 27 Guest Lecture

August 7

Discussion 14 Final Review

August 8

Final Exam Final