Principles and Techniques of Data Science

UC Berkeley, Fall 2020

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  • 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)

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

Ch. 8

Sep 23

Discussion 4 Discussion 4

Sep 24

Lecture 9 Visualization I

Ch. 6.1-6.3

Sep 25

Homework 3 Bike Sharing

Week 6

Sep 28

Lab 5 Lab 5

Sep 29

Lecture 10 Visualization II

Ch. 6.4-6.6

Sep 30

Discussion 5 Discussion 5

Oct 1

Lecture 11 Modeling

Ch. 10

Oct 2

Homework 4 Trump Tweets

Week 7

Oct 5

Lab 6 Lab 6

Oct 6

Lecture 12 Simple Linear Regression

Ch. 13.1-13.3

Oct 7

Discussion 6 Discussion 6

Oct 8

Lecture 13 Ordinary Least Squares

Ch. 13.4

Oct 9

Homework 5 Regression

Week 8

Oct 12

Lab 7 Lab 7

Oct 13

Lecture 14 Midterm Review

Oct 14

Discussion 7 Discussion 7

Oct 15

Exam Midterm (7-9PM PDT)

Oct 16

N/A

Week 9

Oct 19

Lab 8 Lab 8

Oct 20

Lecture 15 Feature Engineering

Ch. 14

Oct 21

Discussion 8 Discussion 8

Oct 22

Lecture 16 Variance

Oct 23

Homework 6 Housing

Week 10

Oct 26

Lab 9 Lab 9

Oct 27

Lecture 17 Bias and Variance

12.3, 15.1-15.2

Oct 28

Discussion 9 Discussion 9

Oct 29

Lecture 18 Regularization & Cross-Validation

Ch. 16, Ch. 15.3

Oct 30

TBD

Week 11

Nov 2

Lab 10 Lab 10

Nov 3

Lecture 19 Gradient Descent

Ch. 11

Nov 4

Discussion 10 Discussion 10

Nov 5

Lecture 20 Logistic Regression I

Ch. 17.1-17.3

Nov 6

Homework 7 Gradient Descent and Logistic Regression

Week 12

Nov 9

Lab 11 Lab 11

Nov 10

Lecture 21 Logistic Regression II, Classification

Ch. 17.4-17.7

Nov 11

Discussion 11 Discussion 11

Nov 12

Lecture 22 Decision Trees

Nov 13

Project 2 Spam/Ham

Week 13

Nov 16

Lab 12 Lab 12

Nov 17

Lecture 23 Inference for Modeling

Ch. 18.1, 18.3

Nov 18

Discussion 12 Discussion 12

Nov 19

Lecture 24 Dimensionality Reduction

Nov 20

Project 2 Spam/Ham

Week 14

Nov 23

Lab 13 Lab 13

Nov 24

Lecture 25 PCA

Nov 25

N/A (Thanksgiving)

Nov 26

N/A (Thanksgiving)

Nov 27

Homework 8 PCA

Week 15

Nov 30

Lab 13 Lab 13

Dec 1

Lecture 26 Clustering

Dec 2

Discussion 13 Discussion 13

Dec 3

Lecture 27 Big Data, Conclusion

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 PDT)