Principles and Techniques of Data Science

UC Berkeley, Spring 2022

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  • All discussions (Week 1), Lab Help Sections (Week 2), and office hours (Week 2) will be held online for the first two weeks (through 1/28). Zoom links, discussions sign up link is on Ed here. In-person sections will go back to in-person starting the week of 1/31; lecture will stay hybrid.
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  • Note: The schedule of lectures and assignments is subject to change.



Schedule

Week 1

Jan 18

Lecture 1 Course Overview

Ch. 1

Weekly Check 1 Weekly Check 1 (due Jan 24)

Jan 20

Lecture 2 Sampling and Probability

Ch. 2, 3.1

Jan 21

Discussion 1 Intro (solutions)

Lab 1 Prerequisite Coding (due Jan 25)

Homework 1 Intro + Prerequisites (due Jan 27)

Week 2

Jan 24

Weekly Check 2 Weekly Check 2 (due Jan 31)

Jan 25

Lecture 3 Pandas I

Ch. 6.1, 6.5

Jan 27

Lecture 4 Pandas II

Ch. 6.2-6.4

Jan 28

Discussion 2 Pandas

Lab 2 Pandas (due Feb 1)

Homework 2 Food Safety (due Feb 3)

Week 3

Jan 31

Weekly Check 3 Weekly Check 3 (due Feb 7)

Feb 1

Lecture 5 Data Cleaning, EDA

Ch. 8-10

Feb 3

Lecture 6 Regex

Ch. 13

Feb 4

Discussion 3 Data Cleaning and Regex

Lab 3 Data Cleaning (due Feb 8)

Homework 3 Tweets (due Feb 10)

Week 4

Feb 7

Weekly Check 4 Weekly Check 4 (due Feb 14)

Feb 8

Lecture 7 Visualization I

Ch. 11.1-11.3

Feb 10

Lecture 8 Visualization II

Ch. 11.4-11.6

Feb 11

Discussion 4 Visualization

Lab 4 Transformations and KDE (due Feb 15)

Homework 4 Bike Sharing (due Feb 17)

Week 5

Feb 14

Weekly Check 5 Weekly Check 5 (due Feb 21)

Feb 15

Lecture 9 Modeling, Loss, and Optimization

Ch. 4

Feb 17

Lecture 10 Simple Linear Regression

Ch. 15

Feb 18

Discussion 5 Modeling and Simple Linear Regression

Lab 5 Modeling, Summary Statistics, Loss Functions (due Feb 22)

Homework 5 Regression (on paper) (due Mar 3)

Week 6

Feb 21

Weekly Check 6 Weekly Check 6 (due Feb 28)

Feb 22

Lecture 11 Ordinary Least Squares

Ch. 19.1

Feb 24

Midterm Midterm 1 (7-9 pm)

Feb 25

Discussion 6 Ordinary Least Squares

Lab 6 Simple Linear Regression (due Mar 1)

Week 7

Feb 28

Weekly Check 7 Weekly Check 7 (due Mar 7)

Mar 1

Lecture 12 Gradient Descent, sklearn

Ch. 17

Mar 3

Lecture 13 Case Study (HCE): Fairness in Housing Appraisal

Mar 4

Discussion 7 Gradient Descent and Feature Engineering

Lab 7 Ordinary Least Squares, Feature Engineering (due Mar 8)

Proj 1A Housing I (due Mar 10)

Week 8

Mar 7

Weekly Check 8 Weekly Check 8 (due Mar 14)

Mar 8

Lecture 14 Feature Engineering

Ch. 20

Mar 10

Lecture 15 Regularization and Cross-Validation

Ch. 22, 21.3

Mar 11

Discussion 8 HCE, Regularization, and Cross-Validation

Lab 8 Cross-Validation and Regularization (due Mar 15)

Proj 1B Housing II (due Mar 17)

Week 9

Mar 14

Weekly Check 9 Weekly Check 9 (due Mar 21)

Mar 15

Lecture 16 Probability I: Random Variables

Ch. 3.2-3.5, 16.1

Mar 17

Lecture 17 Probability II: Estimators, Bias, and Variance

Ch. 16.2-16.3

Mar 18

Discussion 9 Probability

Lab 9 Probability and Modeling (due Mar 29)

Homework 6 Probability and Estimators (due Mar 31)

Spring Break

Mar 22

Spring Break

Mar 24

Spring Break

Week 10

Mar 28

Weekly Check 10 Weekly Check 10 (due Apr 4)

Mar 29

Lecture 18 SQL I

Ch. 7.1-7.2, 7.5

Mar 31

Lecture 19 SQL II

Ch. 7.3-7.4

Apr 1

Discussion 10 SQL

Lab 10 SQL (due Apr 5)

Homework 7 SQL and PCA (due Apr 14)

Apr 2

Grad Project Grad Project Released

Week 11

Apr 4

Weekly Check 11 Weekly Check 11 (due Apr 11)

Apr 5

Lecture 20 PCA

Ch. 26

Apr 7

Midterm Midterm 2 (7-8:30 pm)

Apr 8

Discussion 11 More SQL, PCA

Lab 11 PCA (due Apr 12)

Week 12

Apr 11

Weekly Check 12 Weekly Check 12 (due Apr 18)

Apr 12

Lecture 21 Classification and Logistic Regression

Ch. 24.1-24.3

Apr 14

Lecture 22 Logistic Regression II

Ch. 24.4-24.8

Apr 15

Discussion 12 Logistic Regression

Lab 12 Logistic Regression (due Apr 19)

Proj 2A Spam & Ham I (due Apr 21)

Week 13

Apr 18

Weekly Check 13 Weekly Check 13 (due Apr 25)

Apr 19

Lecture 23 TBD

Apr 21

Lecture 24 Decision Trees

Ch. 27

Apr 22

Discussion 13 Decision Trees

Lab 13 Decision Trees & Random Forests (due Apr 26)

Proj 2B Spam & Ham II (due Apr 28)

Week 14

Apr 25

Weekly Check 14 Weekly Check 14 (due May 2)

Apr 26

Lecture 25 Clustering

Ch. 28

Apr 27

Grad Project First Draft for Grad Project Due

Apr 28

Lecture 26 Inference for Modeling

Ch. 19.2

Apr 29

Discussion 14 Clustering, Inference for Modeling

Week 15

May 3

RRR Week

May 5

RRR Week

Week 16

May 9

Grad Project Final Draft of Grad Project Due

May 13

Final Final Exam (7-10 pm)