# Principles and Techniques of Data Science

UC Berkeley, Fall 2020

**All announcements are on Piazza. Make sure you are enrolled and active there.**- 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)- 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)- Sep 2
**Discussion 1**Linear Algebra and Probability (video) (solutions)- Sep 3
**Lecture 3**Random Variables (QC due Sept. 8)- 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)- Sep 9
**Discussion 2**Random Variables and SQL (video) (solutions)- Sep 10
**Lecture 5**Pandas I (QC due Sept. 14)- 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)- Sep 16
**Discussion 3**Pandas (video) (solutions)- Sep 17
**Lecture 7**Data Cleaning and EDA (QC due Sept. 21)- 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)- Sep 23
**Discussion 4**Regex (notebook) (video) (solutions)- Sep 24
**Lecture 9**Visualization I (QC due Sept. 28)- 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- Sep 30
**Discussion 5**Discussion 5- Oct 1
**Lecture 11**Modeling- Oct 2
**Homework 4**Trump Tweets

### Week 7

- Oct 5
**Lab 6**Lab 6- Oct 6
**Lecture 12**Simple Linear Regression- Oct 7
**Discussion 6**Discussion 6- Oct 8
**Lecture 13**Ordinary Least Squares- 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- Oct 21
**Discussion 8**Discussion 8- Oct 22
**Lecture 16**Variance- Oct 23
**Homework 6**Housing

### Week 10

### Week 11

- Nov 2
**Lab 10**Lab 10- Nov 3
**Lecture 19**Gradient Descent- Nov 4
**Discussion 10**Discussion 10- Nov 5
**Lecture 20**Logistic Regression I- 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- 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- 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)