Data 100

Principles and Techniques of Data Science

UC Berkeley, Spring 2021

Andrew Bray
Joseph E. Gonzalez
  • 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 spring, 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.
  • Live events and lectures are highlighted in green and asynchronous ones in blue.
  • Note: The schedule of lectures and assignments is subject to change.


Week 1

Jan 19

Fireside Chat 1 Introduction, Course Overview (Recording)

Ch. 1

Homework 0 Gather and Logistics (due Jan 21)

Jan 20

N/A

Jan 21

Lecture 2 Data Sampling and Probability

Ch. 2

Jan 22

Lab 1 Prerequisite Coding (due Jan 28)

Homework 1 Prerequisites (due Jan 29)

Week 2

Jan 25

Mini-Discussion 1 Introduction

Jan 26

Lecture 3 Estimation and Bias (Slides)

Ch. 15.1-15.2

Jan 27

N/A

Jan 28

Lecture 4 SQL

Ch. 5

Jan 29

Lab 2 SQL (due Feb 4)

Homework 2 Trump Sampling (due Feb 4)

Week 3

Feb 1

Discussion 1 RVs, Sampling, and SQL (Notebook) (Solutions)

Feb 2

Fireside Chat 2 SQL and Pandas (Recording)

Lecture 5 Pandas I

Ch. 6

Feb 3

N/A

Feb 4

Lecture 6 Pandas II

Ch. 6

Feb 5

Lab 3 Pandas (due Feb 11)

Homework 3 Food Safety (due Feb 11)

Week 4

Feb 8

Mini-Discussion 2

Feb 9

Fireside Chat 3 DataFrames and Data Pipelines (Recording)

Lecture 7 Data Cleaning and EDA

Ch. 7, Ch. 8, Ch. 9

Feb 10

N/A

Feb 11

Lecture 8 Regular Expressions

Ch. 12

Feb 12

Lab 4 Data Cleaning and EDA (due Feb 18)

Homework 4 Tweets (due Feb 18)

Week 5

Feb 15

N/A (President’s Day)

Feb 16

Fireside Chat 4

Lecture 9 Visualization I

Ch. 10.1-10.3

Feb 17

N/A

Feb 18

Lecture 10 Visualization II

Ch. 10.4-10.6

Feb 19

Lab 5 Transformations and KDEs (due Feb 25)

Homework 5 Bike Sharing (due Feb 25)

Week 6

Feb 22

Mini-Discussion 3

Feb 23

Fireside Chat 5 (Recording)

Lecture 11 Modeling

Ch. 3

Feb 24

N/A

Feb 25

Lecture 12 Simple Linear Regression

Ch. 14.1-14.3

Feb 26

Lab 6 Modeling, Summary Statistics, and Loss Functions (due Mar 4)

Homework 6 Regression (notebook) (due Mar 11)

Week 7

Mar 1

Discussion 2 Modeling and Regex (Solutions)

Mar 2

Fireside Chat 6 code (launch, Interactive HTML)

Lecture 13 Ordinary Least Squares

Ch. 14.4

Mar 3

N/A

Mar 4

Review Sessions Midterm Review

Mar 5

Lab 7 Simple Linear Regression (due Mar 11)

Week 8

Mar 8

Mini-Discussion 4

Mar 9

Fireside Chat 7

Exam Midterm (7-9PM PDT)

Mar 10

N/A

Mar 11

Lecture 14 Feature Engineering

Ch. 19

Mar 12

Lab 8 Multiple Linear Regression and Feature Engineering (due Mar 18)

Homework 7 Housing (due Mar 28)

(Optional) Homework 7 Contest Build Your Own Model (due April 4)

Week 9

Mar 15

Discussion 3 Feature Engineering and OLS

Mar 16

Fireside Chat 8

Lecture 15 Bias and Variance

Ch. 15.3, 20.1-20.2

Mar 17

N/A

Mar 18

Lecture 16 Cross-Validation and Regularization

Ch. 21, Ch. 20.3

Mar 19

Lab 9 Feature Engineering and Cross-Validation (due Apr 1)

Week 10

Mar 22

N/A (Spring Break)

Mar 23

N/A (Spring Break)

Mar 24

N/A (Spring Break)

Mar 25

N/A (Spring Break)

Mar 26

N/A (Spring Break)

Week 11

Mar 29

Mini-Discussion 5

Mar 30

Fireside Chat 9

Lecture 17 Modeling in Context: Fairness in Housing Appraisal

Ch. 16

Mar 31

N/A

Apr 1

Lecture 18 Gradient Descent

Ch. 16

Apr 2

Homework 8 HCE and Gradient Descent (due Apr 8)

Apr 3

Graduate Project Guidelines & Requirements

Week 12

Apr 5

Discussion 4 Gradient Descent and HCE (Solutions)

Apr 6

Fireside Chat 10

Lecture 19 Logistic Regression I

Ch. 23.4-23.7

Apr 7

N/A

Apr 8

Lecture 20 Logistic Regression II, Classification

Ch. 26

Apr 9

Lab 10 Logistic Regression (due Apr 15)

Homework 9 Spam/Ham I (due Apr 15)

Week 13

Apr 12

Mini-Discussion 6

Apr 13

Fireside Chat 11

Lecture 21 Decision Trees

Appendix 3

Apr 14

N/A

Apr 15

Lecture 22 Inference for Modeling

Ch. 27

Apr 16

Lab 11 Decision Trees and Random Forests (due Apr 22)

Homework 10 Spam/Ham II (due Apr 22)

Week 14

Apr 19

Discussion 5 Classification (Solutions)

Apr 20

Fireside Chat 12 (Deep Learning) (Recording, Interactive Notebook, Code)

Lecture 23 Principal Component Analysis

Ch. 25

Apr 21

N/A

Apr 22

Lecture 24 Clustering

Apr 23

Lab 12 Principal Component Analysis (due Apr 29)

Homework 11 Principal Component Analysis (due Apr 29)

Week 15

Apr 26

Mini-Discussion 7

Apr 27

Fireside Chat 13

Lecture 25 Big Data

Apr 28

N/A

Apr 29

Lecture 26 Conclusion Live Webinar (slides)

Apr 30

Lab 13 Using the Bootstrap for Estimation (due May 6)

Homework 12 Bonus Assignment (due May 6)