Data 100: Principles and Techniques of Data Science
UC Berkeley, Fall 2023
Ed Datahub Gradescope Extenuating Circumstances
Welcome to Week 5 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
 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
 Lecture Participation 8 Lecture Participation 8

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

 Exam Prep 3 Visualization
 Solution
 Lab 4 Transformations (due Sep 26)
 Homework 4 Bike Sharing (due Sep 28)
Week 6
 Sep 26
 Lecture 10 Modeling and SLR
 Discussion 5 Probability, Sampling, and Visualization
 Sep 28
 Lecture 11 Constant model, Loss, and Transformations
 Sep 29
 Lab 5 Modeling, Summary Statistics, and Loss Functions (due Oct 3)
 Homework 5 Modeling (due Oct 5)
Week 7
 Oct 3
 Lecture 12 Ordinary Least Squares
 Discussion 6 Models
 Oct 5
 Lecture 13 Gradient descent and Sklearn
 Oct 6
 Lab 6 Ordinary Least Squares (due Oct 10)
 Homework 6 Regression (due Oct 12)
Week 8
 Oct 10
 Lecture 14 Feature Engineering
 Discussion 7 OLS and Gradient Descent
 Oct 12
 Lecture 15 Case study (HCE): CCAO
 Oct 13
 Lab 7 Gradient descent and Sklearn (due Oct 17)
 Project A1 Housing I (due Oct 19)
Week 9
 Oct 17
 Lecture 16 CrossValidation and Regularization
 Discussion 8 Feature Engineering and Housing
 Oct 18
 Midterm Midterm (7:00pm  9:00pm)
 Oct 19
 Lecture 17 Random Variables
 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
 Discussion 9 CrossValidation and Regularization
 Oct 26
 Lecture 19 TBD
 Oct 27
 Lab 9 Probability (due Oct 31)
 Homework 7 Probability (due Nov 2)
Week 11
 Oct 31
 Lecture 20 SQL I
 Discussion 10 RVs, Bias, and Variance
 Nov 2
 Lecture 21 SQL II / Cloud Data
 Nov 3
 Lab 10 SQL (due Nov 7)
 Homework 8 SQL (due Nov 9)
Week 12
 Nov 7
 Lecture 22 Logistic Regression I
 Discussion 11 SQL
 Nov 9
 Lecture 23 Logistic Regression II
 Nov 10
 Lab 11 Logistic Regression (due Nov 14)
 Project B1 Spam and Ham I (due Nov 16)
Week 13
 Nov 14
 Lecture 24 Case Study: Climate & Physical Data
 Discussion 12 Logistic Regression I
 Nov 16
 Lecture 25 PCA I
 Nov 17
 Lab 12 Inference & Climate (due Nov 21)
 Project B2 Spam and Ham II (due Nov 30)
Week 14
 Nov 21
 Lecture 26 PCA II
 Discussion 13 Logistic Regression II
 Nov 23
 Lecture No Lecture (Thanksgiving)
 Nov 24
 Lab 13 PCA (due Nov 28)
Week 15
 Nov 28
 Lecture 27 KMeans Clustering
 Discussion 14 PCA
 Nov 30
 Lecture 28 Closing
 Dec 1
 Lab 14 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)