Principles and Techniques of Data Science

UC Berkeley, Fall 2022

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  • The Syllabus contains a detailed explanation of how each course component will work this Fall, please take time to take a look.
  • Note: The schedule of lectures and assignments is subject to change.

Schedule

Week 1

Aug 25

Lecture 1 Introduction

Quick Check 1 Quick Check 1 (due Aug 29)

Aug 26

Lab 1 Prerequisite Coding (due Aug 30)

walkthrough, solution

Homework 1 Prerequisite Math (due Sep 1)

Week 2

Aug 30

Lecture 2 Pandas I

Ch. 6.1, 6.5

Textbook: Pandas Reference Table

Reference: Pandas API Documentation

Discussion 1 Prerequisite

Solution, Recording

Sep 1

Lecture 3 Pandas II

Ch. 6.2-6.4

Quick Check 2 Quick Check 2 (due Sep 6)

Sep 2

Lab 2 Pandas (due Sep 7)

Homework 2 Food Safety (due Sep 9)

Week 3

Sep 6

Lecture 4 Data Cleaning and EDA

Ch. 8-9

Discussion 2 Pandas written questions, coding questions

written sol pdf, written sol notebook, coding sol pdf, coding sol notebook, recording

Sep 8

Lecture 5 Regex

Ch. 13

Quick Check 3 Quick Check 3 (due Sep 12)

Sep 9

Exam prep 1 Pandas and Linear Algebra

Solution

Lab 3 Data Cleaning and EDA and Regex (due Sep 13)

Homework 3 Tweets (due Sep 15)

Week 4

Sep 13

Lecture 6 Visualization I

Ch. 11.1-11.3

Textbook: Seaborn Reference Table

Textbook: Matplotlib Reference Table

Discussion 3 EDA and Regex written questions, coding questions

written sol pdf, coding sol pdf, coding sol notebook, recording

Sep 15

Lecture 7 Visualization II

Ch. 11.4-11.6

Quick Check 4 Quick Check 4

Sep 16

Exam prep 2 EDA and Regex

Solution

Lab 4 Transformation and KDEs (due Sep 20)

Homework 4 Bike Sharing (due Sep 22)

Week 5

Sep 20

Lecture 8 Sampling and probability

Ch. 1, 2, 3.1

Discussion 4 Visualization and Transformation written questions, coding questions

written sol pdf, coding sol pdf, coding sol notebook, recording

Sep 22

Lecture 9 Modeling, SLR

Ch. 15.1-15.2

Quick Check 5 Quick Check 5 (due Sep 26; release at 11am)

Sep 23

Exam prep 3 Visualization

Solution

Lab 5 Modeling, Summary Statistics, and Loss Functions(due Sep 27)

Homework 5 Modeling (due Sep 29)

Week 6

Sep 27

Lecture 10 Constant model, loss, and transformations

Ch. 4

Discussion 5 TBD

Sep 29

Lecture 11 OLS (multiple regression)

Ch. 15.3-15.4

Quick Check 6 Quick Check 6

Sep 30

Lab 6 OLS

Homework 6 Regression (on paper)

Week 7

Oct 4

Lecture 12 Gradient descent / sklearn

Ch. 20

Discussion 6 TBD

Oct 6

Lecture 13 Feature engineering

Ch. 15.5

Quick Check 7 Quick Check 7

Oct 7

Lab 7 Gradient descent / sklearn

Project 1A Housing I

Week 8

Oct 11

Lecture 14 Case Study (HCE): Fairness in Housing Appraisal

Discussion 7 TBD

Oct 13

Lecture 15 Cross-validation + Regularization

Ch. A6, A5.3

Quick Check 8 Quick Check 8

Oct 14

Lab 8 Model Selection, Regularization, and Cross-Validation

Project 1B Housing II

Week 9

Oct 18

Lecture 16 Climate & Physical Data

Discussion 8 TBD

Oct 19

midterm Midterm Exam (7-9 PM)

Oct 20

Lecture 17 Causality

Quick Check 9 Quick Check 9

Oct 21

Lab 9 Climate

Project 1B Housing II

Week 10

Oct 25

Lecture 18 Probability I

Ch. 3.2-3.5, 16.3

Discussion 9 TBD

Oct 27

Lecture 19 Probability II

Ch. 16.1, Ch. 16.4, 19.2

Quick Check 10 Quick Check 10

Oct 28

Lab 10 Probability & Modeling

Homework 7 Probability

Week 11

Nov 1

Lecture 20 SQL I

Ch. 7.1-7.2, 7.5

Discussion 10 TBD

Nov 3

Lecture 21 PCA

Ch. 22

Quick Check 11 Quick Check 11

Nov 4

Lab 11 PCA

Homework 9 PCA

Week 12

Nov 8

Lecture 22 SQL II and Cloud Data

Ch. 7.3-7.4

Discussion 11 TBD

Nov 10

Lecture 23 Environmental DS

Quick Check 12 Quick Check 12

Nov 11

Lab 12 SQL

Homework 9 SQL

Week 13

Nov 15

Lecture 24 Logistic regression I

Ch. 19.1-19.3

Discussion 912 TBD

Nov 17

Lecture 25 Logistic regression II

Ch. 19.4-19.8

Quick Check 13 Quick Check 13

Nov 18

Lab 13 Logistic regression

Project 2A Spam & Ham I

Week 14

Nov 22

Lecture 26 Decision Trees

Ch. 23

Discussion 13 TBD

Lab 13 Decision Trees & Random Forests

Project 2B Spam & Ham II

Nov 24

No Lecture THANKSGIVING

Week 15

Nov 29

Lecture 27 Clustering

Ch. 24

Discussion 14 TBD

Lab 14 Clustering

Dec 1

Lecture 28 Closing Lecture

Week 16

Dec 5

RRR

Dec 6

RRR

Dec 7

RRR

Dec 8

RRR

Dec 9

RRR

Week 17

Dec 13

final Final Exam (3-6 PM)