Principles and Techniques of Data Science

UC Berkeley, Fall 2020

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  • 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 fall, 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 @15 on Piazza.


Week 1

Aug 26

N/A

Aug 27

Lecture 1 Introduction, Course Overview (QC due Aug. 31)

Ch. 1

Aug 28

Homework 1 Prerequisites (due Sept. 3)

Week 2

Aug 31

Lab 1 Prerequisite Coding (due Aug. 31)

Sep 1

Lecture 2 Data Sampling and Probability (QC due Sept. 8)

Ch. 2

Sep 2

Discussion 1 Linear Algebra and Probability (video) (solutions)

Sep 3

Lecture 3 Random Variables (QC due Sept. 8)

Ch. 12.1-12.2

Sep 4

Homework 2 Trump Sampling (due Sept. 10)

Week 3

Sep 8

Lab 2 SQL (due Sept. 8th)

Sep 8

Lecture 4 SQL (QC due Sept. 14)

Ch. 9

Sep 9

Discussion 2 Random Variables and SQL (video) (solutions)

Sep 10

Lecture 5 Pandas I (QC due Sept. 14)

Ch. 3

Sep 11

Project 1 Food Safety (due Sept. 24)

Week 4

Sep 14

Lab 3 Pandas I (due Sept. 14)

Sep 15

Lecture 6 Pandas II (QC due Sept. 21)

Ch. 3

Sep 16

Discussion 3 Pandas (video) (solutions)

Sep 17

Lecture 7 Data Cleaning and EDA (QC due Sept. 21)

Ch. 4.1, Ch. 5

Sep 18

N/A

Week 5

Sep 21

Lab 4 Data Cleaning and EDA (due Sept. 21)

Sep 22

Lecture 8 Regular Expressions (QC due Sept. 28)

Ch. 8

Sep 23

Discussion 4 Regex (notebook) (video) (solutions)

Sep 24

Lecture 9 Visualization I (QC due Sept. 28)

Ch. 6.1-6.3

Sep 25

Homework 3 Bike Sharing (due Oct. 1)

Week 6

Sep 28

Lab 5 Transformations and KDEs (due Sept. 28)

Sep 29

Lecture 10 Visualization II (QC due Oct. 5)

Ch. 6.4-6.6

Sep 30

Discussion 5 Visualizations (notebook) (video) (solutions)

Oct 1

Lecture 11 Modeling (QC due Oct. 5)

Ch. 10

Oct 2

Homework 4 Trump Tweets (due Oct. 8)

Week 7

Oct 5

Lab 6 Modeling, Summary Statistics, and Loss Functions (due Oct. 5)

Oct 6

Lecture 12 Simple Linear Regression (QC due Oct. 12)

Ch. 13.1-13.3

Oct 7

Discussion 6 Modeling and Linear Regression (video) (solutions)

Oct 8

Lecture 13 Ordinary Least Squares (QC due Oct. 12)

Ch. 13.4

Oct 9

Homework 5 Regression (due Oct. 22)

Week 8

Oct 12

Lab 7 Simple Linear Regression (due Oct. 12)

Oct 13

Review Sessions Midterm Review

Oct 14

Discussion 7 Least Squares (video) (solutions)

Oct 15

Exam Midterm (7-9PM PDT)

Oct 16

N/A

Week 9

Oct 19

N/A

Oct 20

Lecture 14 Feature Engineering (QC due Oct. 26)

Ch. 14

Survey Mid-Semester Survey (due Oct. 26)

Oct 21

Discussion 8 Feature Engineering and Midterm Review (video) (solutions)

Oct 22

Lecture 15 Bias and Variance (QC due Oct. 26)

Ch. 12, 15.1-15.2

Oct 23

Homework 6 Housing (due Nov. 6)

Week 10

Oct 26

Lab 8 Multiple Linear Regression and Feature Engineering (due Oct. 26)

Oct 27

Lecture 16 Cross-Validation and Regularization (QC due Nov. 2)

Ch. 16, Ch. 15.3

Oct 28

Discussion 9 Bias & Variance, Cross-Validation, & Regularization (video) (solutions)

Oct 29

Lecture 17 Gradient Descent (QC due Nov. 2)

Ch. 11

Oct 30

N/A

Week 11

Nov 2

Lab 9 Feature Engineering & Cross-Validation (due Nov. 2)

Nov 3

N/A (Election Day)

Nov 4

Discussion 10 Gradient Descent (video) (solutions)

Nov 5

Lecture 18 Logistic Regression I (QC due Nov. 9)

Ch. 17.1-17.3

Nov 6

Homework 7 Gradient Descent and Logistic Regression (due Nov. 12)

Week 12

Nov 9

Lab 10 Logistic Regression (due Nov. 9)

Graduate Project Graduate Project

Nov 10

Lecture 19 Logistic Regression II, Classification (QC due Nov. 16)

Ch. 17.4-17.7

Nov 11

Discussion 11 Logistic Regression (video) (solutions)

Nov 12

Lecture 20 Decision Trees (QC due Nov. 16)

Nov 13

Project 2 Spam/Ham (due Nov. 30)

Week 13

Nov 16

Lab 11 Decision Trees and Random Forests (due Nov. 16)

Nov 17

Lecture 21 Inference for Modeling (QC due Nov. 23)

Ch. 18.1, 18.3

Nov 18

Discussion 12 Decision Trees & Inference (video)

Nov 19

Lecture 22 Principal Components Analysis (QC due Nov. 23)

Nov 20

Homework 8 PCA (due Dec. 3)

Week 14

Nov 23

Lab 12 Principal Component Analysis (due Nov. 23)

Live Session AMA with Professors (9-10AM PST)

Nov 24

Lecture 23 Clustering (QC due Nov. 30)

Nov 25

N/A (Thanksgiving)

Nov 26

N/A (Thanksgiving)

Nov 27

N/A

Week 15

Nov 30

Lab 13 Using the Bootstrap for Estimation (due Dec. 7)

Dec 1

Lecture 24 Big Data (QC due Dec. 7)

Dec 2

Discussion 13 PCA, Clustering, & Big Data (video)

Dec 3

Lecture 25 Conclusion (live, 9:30-11AM PST)

Dec 4

N/A

Week 16 (RRR Week)

Dec 8

Review

Dec 10

Review

Week 17 (Finals Week)

Dec 15

Exam Final Exam (7-10PM PST)