Principles and Techniques of Data Science
UC Berkeley, Spring 2021
- 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 spring, 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 @6 on Piazza.
- Live events and lectures are highlighted in green and asynchronous ones in blue.
- Note: The schedule of lectures and assignments is subject to change.
Week 1
- Jan 19
Fireside Chat 1 Introduction, Course Overview (Recording)
Homework 0 Gather and Logistics (due Jan 21)
- Jan 20
N/A
- Jan 21
Lecture 2 Data Sampling and Probability
- Jan 22
Lab 1 Prerequisite Coding (due Jan 28)
Homework 1 Prerequisites (due Jan 29)
Week 2
- Jan 25
Mini-Discussion 1 Introduction
- Jan 26
Lecture 3 Estimation and Bias (Slides)
- Jan 27
N/A
- Jan 28
Lecture 4 SQL
- Jan 29
Lab 2 SQL (due Feb 4)
Homework 2 Trump Sampling (due Feb 4)
Week 3
- Feb 1
Discussion 1 RVs, Sampling, and SQL (Notebook) (Solutions)
- Feb 2
Fireside Chat 2 SQL and Pandas (Recording)
Lecture 5 Pandas I
- Feb 3
N/A
- Feb 4
Lecture 6 Pandas II
- Feb 5
Lab 3 Pandas (due Feb 11)
Homework 3 Food Safety (due Feb 11)
Week 4
- Feb 8
Mini-Discussion 2
- Feb 9
Fireside Chat 3 DataFrames and Data Pipelines (Recording)
Lecture 7 Data Cleaning and EDA
- Feb 10
N/A
- Feb 11
Lecture 8 Regular Expressions
- Feb 12
Lab 4 Data Cleaning and EDA (due Feb 18)
Homework 4 Tweets (due Feb 18)
Week 5
- Feb 15
N/A (President’s Day)
- Feb 16
Fireside Chat 4
Lecture 9 Visualization I
- Feb 17
N/A
- Feb 18
Lecture 10 Visualization II
- Feb 19
Lab 5 Transformations and KDEs (due Feb 25)
Homework 5 Bike Sharing (due Feb 25)
Week 6
Week 7
- Mar 1
Discussion 2
- Mar 2
Fireside Chat 6
Lecture 13 Ordinary Least Squares
- Mar 3
N/A
- Mar 4
Review Sessions Midterm Review
- Mar 5
Lab 7 Simple Linear Regression (due Mar 11)
Week 8
- Mar 8
Mini-Discussion 4
- Mar 9
Fireside Chat 7
Exam Midterm (7-9PM PDT)
- Mar 10
N/A
- Mar 11
Lecture 14 Feature Engineering
- Mar 12
Lab 8 Multiple Linear Regression and Feature Engineering (due Mar 18)
Week 9
Week 10
- Mar 22
N/A (Spring Break)
- Mar 23
N/A (Spring Break)
- Mar 24
N/A (Spring Break)
- Mar 25
N/A (Spring Break)
- Mar 26
N/A (Spring Break)
Week 11
- Mar 29
Mini-Discussion 5
- Mar 30
Fireside Chat 9
Lecture 17 Gradient Descent
- Mar 31
N/A
- Apr 1
Lecture 18 Logistic Regression I
- Apr 2
Lab 10 Logistic Regression (due Apr 8)
Homework 8 Gradient Descent and Logistic Regression (due Apr 8)
Week 12
- Apr 5
Discussion 4
- Apr 6
Fireside Chat 10
Lecture 19 Logistic Regression II, Classification
- Apr 7
N/A
- Apr 8
Lecture 20 Decision Trees
- Apr 9
Lab 11 Decision Trees and Random Forests (due Apr 15)
Homework 9 Spam/Ham I (due Apr 15)
Week 13
- Apr 12
Mini-Discussion 6
- Apr 13
Fireside Chat 11
Lecture 21 Inference for Modeling
- Apr 14
N/A
- Apr 15
Lecture 22 Clustering
- Apr 16
Lab 12 TBD (due Apr 22)
Homework 10 Spam/Ham II (due Apr 22)
Week 14
- Apr 19
Discussion 5
- Apr 20
Fireside Chat 12
Lecture 23 Principal Component Analysis
- Apr 21
N/A
- Apr 22
Lecture 24 Big Data
- Apr 23
Lab 13 Principal Component Analysis (due Apr 29)
Homework 11 Principal Component Analysis (due Apr 29)
Week 15
- Apr 26
Mini-Discussion 7
- Apr 27
Fireside Chat 13
Lecture 25 TBD
- Apr 28
N/A
- Apr 29
Lecture 26 Conclusion
- Apr 30
Lab 14 Using the Bootstrap for Estimation (due May 6)
Homework 12 Taxis (due May 6)