Data 100: Principles and Techniques of Data Science
UC Berkeley, Fall 2024
Ed Datahub Gradescope Lectures Playlist Additional Accommodations Office Hours Queue
Welcome to Week 1 of Data 100!
Lectures will be webcast at: https://berkeley.zoom.us/j/91349586134.
Schedule
Week 1
- Aug 29
-
- Lecture 1 Introduction
- Note 1
- Lecture Participation 1 Lecture Participation 1
- Aug 30
- Lab 1 Prerequisite Coding (due Sep 3)
- Homework 1A Plotting and the Permutation Test (due Sep 5)
- Homework 1B Prerequisite Math (due Sep 5)
Week 2
- Sep 3
- Lecture 2 Pandas I
- Discussion 1 Prerequisites
- Sep 5
- Lecture 3 Pandas II
- Sep 6
- Lab 2A Pandas (due Sep 10)
- Homework 2A Food Safety (due Sep 12)
- Drop Deadline (w/o fee) 11:59pm PT
Week 3
- Sep 10
- Lecture 4 Pandas III
- Discussion 2 Pandas I
- Sep 12
- Lecture 5 Data Cleaning and EDA
- Sep 13
- Lab 2B Data Cleaning and EDA (due Sep 17)
- Homework 2B Food Safety II (due Sep 19)
- Add Deadline (w/o fee) 11:59pm PT
Week 4
- Sep 17
- Lecture 6 Regex
- Discussion 3 Pandas II, EDA
- Sep 19
- Lecture 7 Visualization I
- Sep 20
- Lab 3 Regex, EDA (due Sep 24)
- Homework 3 Tweets (due Sep 26)
- Drop Deadline (with fee) 11:59pm PT
Week 5
- Sep 24
- Lecture 8 Visualization II
- Discussion 4 Regex, Visualization, and Transformation
- Sep 26
- Lecture 9 Sampling
- Sep 27
- Lab 4 Transformations (due Oct 1)
- Homework 4 Bike Sharing (due Oct 3)
- Add Deadline for Graduate Students 11:59pm PT
Week 6
- Oct 1
- Lecture 10 Modeling, SLR
- Discussion 5 Probability, Sampling, and Visualization
- Oct 3
- Lecture 11 Constant model, Loss, and Transformations
- Oct 4
- Lab 5 Modeling, Summary Statistics, and Loss Functions (due Oct 8)
- Homework 5 Modeling (due Oct 10)
Week 7
- Oct 8
- Lecture 12 OLS (Multiple Regression)
- Discussion 6 Models
- Oct 10
- Lecture 13 Gradient descent / sklearn
- Oct 11
- Lab 6 OLS (due Oct 15)
- Project A1 Housing I (due Oct 24)
Week 8
- Oct 15
- Lecture 14 Feature Engineering
- Discussion 7 OLS, Gradient Descent
- Oct 17
- Lecture 15 Case Study (HCE): CCAO
- Oct 18
- Lab 7 Gradient descent and Sklearn (due Oct 22)
Week 9
- Oct 22
- Lecture 16 Cross-Validation and Regularization
- Discussion 8 Feature Engineering, Housing
- Oct 23
- Midterm Exam Midterm (7-9 PM PST)
- Oct 24
- Lecture 17 Random Variables
- Oct 25
- Lab 8 Model Selection (due Oct 29)
- Project A2 Housing II (due Oct 31)
Week 10
- Oct 29
- Lecture 18 Estimators, Bias, and Variance
- Discussion 9 Cross-Validation and Regularization
- Oct 31
- Lecture 19 Parameter Inference & the Bootstrap
- Nov 1
- Lab 9 Probability (due Nov 5)
- Homework 6 Probability (due Nov 7)
- Grade Option Change Deadline 11:59pm PT
Week 11
- Nov 5
- Lecture 20 SQL I
- Discussion 10 RVs, Bias, and Variance
- Nov 7
- Lecture 21 SQL II
- Nov 8
- Lab 10 SQL (due Nov 12)
- Homework 7 SQL (due Nov 14)
Week 12
- Nov 12
- Lecture 22 Logistic Regression I
- Discussion 11 SQL
- Nov 14
- Lecture 23 Logistic Regression II
- Nov 15
- Lab 11 Logistic Regression (due Nov 19)
- Project B1 Spam & Ham I (due Nov 21)
Week 13
- Nov 19
- Lecture 24 LLMs
- Discussion 12 Logistic Regression
- Nov 21
- Lecture 25 PCA I
- Nov 22
- Project B2 Spam & Ham II (due Dec 5)
Week 14
- Nov 26
- Lecture 26 PCA II
- Discussion 13 PCA
- Nov 28
- No Lecture
- Nov 29
- Lab 12 PCA (due Dec 3)
Week 15
- Dec 3
- Lecture 27 Clustering
- Discussion 14 Clustering
- Dec 5
- Lecture 28 Guest + closing
- Dec 6
- Lab 13 Clustering (due Dec 10)
Week 16
- Dec 9
- RRR
- Dec 10
- RRR
- Dec 11
- RRR
- Dec 12
- RRR
- Dec 13
- RRR
Week 17
- Dec 18
- Final Exam Final (8-11 AM PST)