Data 100: Principles and Techniques of Data Science

UC Berkeley, Fall 2023

Ed Datahub Gradescope Lectures Playlist Extenuating Circumstances

Fernando Pérez

He/Him/His

fernando.perez@berkeley.edu

Office Hours: Tue 11-12pm (Evans 419)

Narges Norouzi

She/Her/Hers

norouzi@berkeley.edu

Office Hours: Mon 11-12pm (Soda 775)

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 2 Pandas I

Note 2

Lecture Participation 2 Lecture Participation 2

Discussion 1 Prerequisites

Solution

Aug 31

Lecture 3 Pandas II

Note 3

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

Discussion 2 Pandas I Worksheet, Notebook

Solution

Sep 7

Lecture 5 Data Cleaning and EDA

Note 5

Lecture Participation 5 Lecture Participation 5

Sep 8

Exam Prep 1 Pandas

Solution worksheet, notebook, recording

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

Exam Prep 2 Pandas II, RegEx worksheet, notebook

Solution worksheet, notebook, recording

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 9 Sampling

Note 9

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

Discussion 5 Visualization worksheet, notebook

Solution

Sep 28

Lecture 11 Constant model, Loss, and Transformations

Note 11

Lecture Participation 11 Lecture Participation 11

Sep 29

Exam Prep 4 Sampling

Solution, recording

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

Exam Prep 5 Modeling

Solution, recording

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 Cross-Validation 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, Cross-Validation, and Regularization

Solution

Oct 26

Lecture 19 Bias, Variance, and Inference in Modeling

Note 19

Lecture Participation 19 Lecture Participation 19

Oct 27

Exam Prep 7 Cross-Validation, Regularization, and Random Variables

Solution, recording

Lab 9 Probability (due Oct 31)

Homework 7 Probability (due Nov 2)

Week 11

Oct 31

Lecture 20 SQL I

Note 20

Lecture Participation 20 Lecture Participation 20

Discussion 10 RVs, Bias, and Variance

Solution

Nov 2

Lecture 21 SQL II

Note 21

Lecture Participation 21 Lecture Participation 21

Nov 3

Exam Prep 8 Bias-Variance 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

Discussion 11 SQL worksheet, notebook

Solution

Nov 9

Lecture 23 Logistic Regression II

Note 23

Lecture Participation 23 Lecture Participation 23

Nov 10

Exam Prep 9 SQL

Solution, recording

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 25 PCA I

Note 25

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 26 PCA II

Note 26

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

Discussion 13 PCA

Solution

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)