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 3 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 Participation 2 Lecture Participation 2

 Discussion 1 Prerequisites
 MiniLecture, Solutions, Walkthrough
 Sep 5
 Lecture Participation 3 Lecture Participation 3
 Exam Prep 1 Pandas
 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
 Note 4
 Lecture Participation 4 Lecture Participation 4

 Discussion 2 Pandas I
 MiniLecture, Solutions, Notebook, Groupwork Notebook, Groupwork Walkthrough, Regular Walkthrough
 Sep 12

 Lecture 5 Data Cleaning and EDA
 Note 5
 Lecture Participation 5 Lecture Participation 5
 Exam Prep 2 Pandas 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 CrossValidation and Regularization
 Discussion 8 Feature Engineering, Housing
 Oct 23
 Midterm Exam Midterm (79 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 CrossValidation 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 (811 AM PST)