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 11 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
 Solutions, Walkthrough
 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
 Solutions, Walkthrough
 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 Participation 6 Lecture Participation 6

 Discussion 3 Pandas II, EDA
 MiniLecture, Solutions, Walkthrough
 Sep 19

 Lecture 7 Visualization I
 Note 7
 Lecture Participation 7 Lecture Participation 7

 Exam Prep 3 RegEx
 Solutions, Walkthrough
 Sep 20
 Lab 3 Regex, EDA (due Sep 24)
 Homework 3 Text Analysis of Bloomberg Articles (due Sep 26)
 Drop Deadline (with fee) 11:59pm PT
Week 5
 Sep 24

 Lecture 8 Visualization II
 Note 8
 Lecture Participation 8 Lecture Participation 8

 Discussion 4 Regex, Visualization, and Transformation
 MiniLecture, Solutions, Walkthrough
 Sep 26
 Lecture Participation 9 Lecture Participation 9

 Exam Prep 4 Data Visualization
 Solutions, Walkthrough
 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
 Note 10
 Lecture Participation 10 Lecture Participation 10

 Discussion 5 Probability, Sampling, and Visualization
 MiniLecture, Solutions, Walkthrough
 Oct 3

 Lecture 11 Constant model, Loss, and Transformations
 Note 11
 Lecture Participation 11 Lecture Participation 11

 Exam Prep 5 SLR
 Solutions, Walkthrough
 Oct 4
 Lab 5 Modeling, Summary Statistics, and Loss Functions (due Oct 8)
 Homework 5A Modeling (due Oct 10)
Week 7
 Oct 8

 Lecture 12 OLS (Multiple Regression)
 Note 12
 Lecture Participation 12 Lecture Participation 12

 Discussion 6 Models, OLS
 MiniLecture, Solutions, Walkthrough
 Oct 10

 Lecture 13 Gradient descent / sklearn
 Note 13
 Lecture Participation 13 Lecture Participation 13

 Exam Prep 6 OLS, Gradient Descent
 Solutions, Walkthrough
 Oct 11
 Lab 6 OLS (due Oct 15)
 Homework 5B OLS (due Oct 17)
Week 8
 Oct 15

 Lecture 14 Feature Engineering
 Note 14
 Lecture Participation 14 Lecture Participation 14
 Oct 17

 Lecture 15 Case Study (HCE): CCAO
 Note 15
 Lecture Participation 15 Lecture Participation 15
 Oct 18
 Lab 7 Gradient descent and Sklearn (due Oct 22)
 Project A1 Housing I (due Oct 25)
Week 9
 Oct 22

 Lecture 16 CrossValidation and Regularization
 Note 16
 Lecture Participation 16 Lecture Participation 16

 Discussion 8 Midterm Review
 Solutions
 Oct 23
 Midterm Exam Midterm (79 PM PST)
 Oct 24

 Lecture 17 Random Variables
 Note 17
 Lecture Participation 17 Lecture Participation 17

 Exam Prep 7 Cross Validation and Regularization
 Solutions, Walkthrough
 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
 Note 18
 Lecture Participation 18 Lecture Participation 18

 Discussion 9 CrossValidation and Regularization
 MiniLecture, Solutions, Walkthrough
 Oct 31

 Lecture 19 Parameter Inference & the Bootstrap
 Note 19
 Lecture Participation 19 Lecture Participation 19

 Exam Prep 8 Probability and BiasVariance
 Solutions
 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 Participation 20 Lecture Participation 20

 Discussion 10 RVs, Bias, and Variance
 MiniLecture, Solutions, Walkthrough
 Nov 7
 Lecture Participation 21 Lecture Participation 21
 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 PCA I
 Discussion 12 Logistic Regression
 Nov 21
 Lecture 25 PCA II
 Nov 22
 Lab 12 PCA (due Nov 26)
 Project B2 Spam & Ham II (due Dec 5)
Week 14
 Nov 26
 Lecture 26 Clustering
 Discussion 13 PCA
 Nov 28
 No Lecture
 Nov 29
 Lab 13 Clustering (due Dec 10)
Week 15
 Dec 3
 Lecture 27 Neural Networks
 Discussion 14 Clustering
 Dec 5
 Lecture 28 LLMs + closing
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)