Data 100: Principles and Techniques of Data Science
UC Berkeley, Fall 2023
Ed Datahub Gradescope Lectures Playlist Extenuating Circumstances
Welcome to Week 15 of Data 100!
Schedule
Week 1
 Aug 24

 Lecture 1 Introduction
 Note 1
 Lecture Participation 1 Lecture Participation 1
 Aug 25
 Lab 1 Prerequisite Coding (due Aug 29)
 Homework 1A Plotting and Permutation Test (due Aug 31)
 Homework 1B Prerequisite Math (due Aug 31)
Week 2
 Aug 29
 Lecture Participation 2 Lecture Participation 2

 Discussion 1 Prerequisites
 Solution
 Aug 31
 Lecture Participation 3 Lecture Participation 3
 Sep 1
 Lab 2A Pandas (due Sep 5)
 Homework 2A Food Safety (due Sep 7)
Week 3
 Sep 5

 Lecture 4 Pandas III
 Note 4
 Lecture Participation 4 Lecture Participation 4
 Sep 7

 Lecture 5 Data Cleaning and EDA
 Note 5
 Lecture Participation 5 Lecture Participation 5
 Sep 8
 Lab 2B Data Cleaning and EDA (due Sep 12)
 Homework 2B Food Safety II (due Sep 14)
Week 4
 Sep 12

 Lecture 6 Regex (and finish EDA)
 Note 6
 Lecture Participation 6 Lecture Participation 6

 Discussion 3 Pandas II, III Worksheet, Groupwork notebook
 Solution Worksheet, Groupwork notebook
 Sep 14

 Lecture 7 Visualization I
 Note 7
 Lecture Participation 7 Lecture Participation 7
 Sep 15
 Lab 3 Regex and EDA (due Sep 19)
 Homework 3 Tweets (due Sep 21)
Week 5
 Sep 19

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

 Discussion 4 EDA, RegEx
 Solution
 Sep 21
 Lecture Participation 9 Lecture Participation 9
 Sep 22

 Exam Prep 3 Visualization
 Solution, recording
 Lab 4 Transformations (due Sep 26)
 Homework 4 Bike Sharing (due Sep 28)
Week 6
 Sep 26

 Lecture 10 Intro to Modeling, SLR
 Note 10
 Lecture Participation 10 Lecture Participation 10
 Sep 28

 Lecture 11 Constant model, Loss, and Transformations
 Note 11
 Lecture Participation 11 Lecture Participation 11
 Sep 29
 Lab 5 Modeling, Summary Statistics, and Loss Functions (due Oct 3)
 Homework 5A Sampling (due Oct 5)
 Homework 5B Modeling (due Oct 5)
Week 7
 Oct 3

 Lecture 12 Ordinary Least Squares
 Note 12
 Lecture Participation 12 Lecture Participation 12

 Discussion 6 Sampling and Modeling
 Solution
 Oct 5

 Lecture 13 Gradient Descent
 Note 13
 Lecture Participation 13 Lecture Participation 13
 Oct 6
 Lab 6 Ordinary Least Squares (due Oct 10)
 Homework 6 Regression (due Oct 12)
Week 8
 Oct 10

 Lecture 14 Sklearn and Feature Engineering
 Note 14
 Lecture Participation 14 Lecture Participation 14

 Discussion 7 OLS, Gradient Descent, and Feature Engineering
 Solution
 Oct 12

 Lecture 15 Case study (HCE): CCAO
 Note 15
 Lecture Participation 15 Lecture Participation 15
 Oct 13

 Exam Prep 6 OLS and Gradient Descent
 Solution, recording
 Lab 7 Gradient descent and Sklearn (due Oct 17)
 Project A1 Housing I (due Oct 21)
Week 9
 Oct 17

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

 Discussion 8 Midterm Review
 Solution
 Oct 18
 Midterm Midterm (7:00pm  9:00pm)
 Oct 19

 Lecture 17 Random Variables
 Note 17
 Lecture Participation 17 Lecture Participation 17
 Oct 20
 Lab 8 Model Selection (due Oct 24)
 Project A2 Housing II (due Oct 26)
Week 10
 Oct 24

 Lecture 18 Estimators, Bias and Variance
 Note 18
 Lecture Participation 18 Lecture Participation 18

 Discussion 9 Housing, CrossValidation, and Regularization
 Solution
 Oct 26

 Lecture 19 Bias, Variance, and Inference in Modeling
 Note 19
 Lecture Participation 19 Lecture Participation 19
 Oct 27
 Lab 9 Probability (due Oct 31)
 Homework 7 Probability (due Nov 2)
Week 11
 Oct 31
 Lecture Participation 20 Lecture Participation 20

 Discussion 10 RVs, Bias, and Variance
 Solution
 Nov 2
 Lecture Participation 21 Lecture Participation 21
 Nov 3

 Exam Prep 8 BiasVariance Tradeoff
 Solution, recording
 Lab 10 SQL (due Nov 7)
 Homework 8 SQL (due Nov 9)
Week 12
 Nov 7

 Lecture 22 Logistic Regression I
 Note 22
 Lecture Participation 22 Lecture Participation 22
 Nov 9

 Lecture 23 Logistic Regression II
 Note 23
 Lecture Participation 23 Lecture Participation 23
 Nov 10
 Lab 11 Logistic Regression (due Nov 14)
 Project B1 Spam and Ham I (due Nov 16)
Week 13
 Nov 14
 Lecture 24 Guest Lecture on Large Language Models
 Lecture Participation 24 Lecture Participation 24

 Discussion 12 Logistic Regression
 Solution
 Nov 16
 Lecture Participation 25 Lecture Participation 25
 Nov 17

 Exam Prep 10 Logistic Regression
 Solution, recording
 Project B2 Spam and Ham II (due Nov 30)
Week 14
 Nov 21
 Lecture Participation 26 Lecture Participation 26
 Discussion No Discussion (Thanksgiving)
 Nov 23
 Lecture No Lecture (Thanksgiving)
 Nov 24
 Lab 12 PCA (due Nov 28)
Week 15
 Nov 28

 Lecture 27 Clustering
 Note 27
 Lecture Participation 27 Lecture Participation 27
 Nov 30
 Lecture 28 Neural Network and Conclusion
 Lecture Participation 28 Lecture Participation 28
 Dec 1

 Exam Prep 11 PCA, Clustering
 Solution, recording
 Lab 13 Clustering (due Dec 5)
Week 16
 Dec 4
 RRR
 Dec 5
 RRR
 Dec 6
 RRR
 Dec 7
 RRR
 Dec 8
 RRR
Week 17
 Dec 14
 Final Exam Final (11:30am  2:30pm)