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) (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)

Oct 22

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

Ch. 12, 15.1-15.2

Oct 23

Homework 6 Housing (due Nov. 5)

Week 10

Oct 26

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

Oct 27

Lecture 16 Regularization & Cross-Validation

Ch. 16, Ch. 15.3

Oct 28

Discussion 9 Discussion 9

Oct 29

Lecture 17 Gradient Descent

Ch. 11

Oct 30

TBD

Week 11

Nov 2

Lab 9 Lab 9

Nov 3

N/A (Election Day)

Nov 4

Discussion 10 Discussion 10

Nov 5

Lecture 18 Logistic Regression I

Ch. 17.1-17.3

Nov 6

Homework 7 Gradient Descent and Logistic Regression

Week 12

Nov 9

Lab 10 Lab 10

Nov 10

Lecture 19 Logistic Regression II, Classification

Ch. 17.4-17.7

Nov 11

Discussion 11 Discussion 11

Nov 12

Lecture 20 Decision Trees

Nov 13

Project 2 Spam/Ham

Week 13

Nov 16

Lab 11 Lab 11

Nov 17

Lecture 21 Inference for Modeling

Ch. 18.1, 18.3

Nov 18

Discussion 12 Discussion 12

Nov 19

Lecture 22 Dimensionality Reduction

Nov 20

N/A

Week 14

Nov 23

Lab 12 Lab 12

Nov 24

Lecture 23 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 24 Clustering

Dec 2

Discussion 13 Discussion 13

Dec 3

Lecture 25 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)