Data 100: Principles and Techniques of Data Science

UC Berkeley, Spring 2024

Ed Datahub Gradescope Lectures Playlist Extenuating Circumstances Office Hours Queue

Joseph E. Gonzalez

He/Him/His

jegonzal@cs.berkeley.edu

Office Hours: Tuesdays from 3:00 to 4:30 in Soda 773 (Starting Jan 23rd)

Narges Norouzi

She/Her/Hers

norouzi@berkeley.edu

Office Hours: Thursdays from 1:00 to 2:30PM in Warren Hall (Room 101-BC)

Welcome to Week 7 of Data 100!

Lectures will be webcast at: https://berkeley.zoom.us/j/91646148607.

Schedule

Week 1

Jan 16

Lecture 1 Introduction

Note 1

Lecture Participation 1 Lecture Participation 1

Jan 18

Lecture 2 Pandas I

Note 2

Lecture Participation 2 Lecture Participation 2

Jan 19

Lab 1 Prerequisite Coding (due Jan 23)

Homework 1A Plotting and Permutation Test (due Jan 25)

Homework 1B Prerequisite Math (due Jan 25)

Week 2

Jan 23

Lecture 3 Pandas II

Note 3

Lecture Participation 3 Lecture Participation 3

Discussion 1 Prerequisites

Solution, Video

Jan 25

Lecture 4 Pandas III

Note 4

Lecture Participation 4 Lecture Participation 4

Jan 26

Lab 2A Pandas (due Jan 30)

Homework 2A Food Safety (due Feb 1)

Week 3

Jan 30

Lecture 5 Data Wrangling and EDA (Part 1)

Note 5

Lecture Participation 5 Lecture Participation 5

Discussion 2 Pandas I, Worksheet Notebook, Groupwork Notebook

Worksheet Solution, Video Groupwork Solution, Video

Feb 1

Lecture 6 Data Wrangling and EDA (Part 2) & Text Wrangling and Regex

Note 6

Lecture Participation 6 Lecture Participation 6

Exam Prep 1 Pandas

Solution, Video

Feb 2

Lab 2B Data Cleaning and EDA (due Feb 6)

Homework 2B Food Safety II (due Feb 8)

Week 4

Feb 6

Lecture 7 Visualization I

Note 7

Lecture Participation 7 Lecture Participation 7

Discussion 3 Pandas II, EDA, Regex

Solution, Video

Feb 8

Lecture 8 Visualization II

Note 8

Lecture Participation 8 Lecture Participation 8

Exam Prep 2 Pandas II, RegEx

Solution, Video

Feb 9

Lab 3 Regex, EDA (due Feb 13)

Homework 3 Text Analysis of Bloomberg Articles (due Feb 15)

Week 5

Feb 13

Lecture 9 Sampling

Note 9

Lecture Participation 9 Lecture Participation 9

Discussion 4 Visualization and Transformation, Worksheet Notebook

Worksheet Solution, Notebook Solution, Video

Feb 15

Lecture 10 Modeling, SLR

Note 10

Lecture Participation 10 Lecture Participation 10

Exam Prep 3 Visualization

Solution, Video

Feb 16

Lab 4 Transformations (due Feb 20)

Homework 4 Bike Sharing (due Feb 22)

Week 6

Feb 20

Lecture 11 Constant model, Loss, and Transformations

Note 11

Lecture Participation 11 Lecture Participation 11

Discussion 5 Probability, Sampling, and Simple Linear Regression

Solution

Feb 22

Lecture 12 OLS (Multiple Regression)

Note 12

Lecture Participation 12 Lecture Participation 12

Exam Prep 4 Sampling

Solution, Video

Feb 23

Lab 5 Modeling, Loss Functions, and Summary Statistics (due Feb 27)

Homework 5 Sampling (due Feb 29)

Week 7

Feb 27

Lecture 13 Gradient descent / sklearn

Note 13

Lecture Participation 13 Lecture Participation 13

Discussion 6 Constant Models, OLS, and Multiple Linear Regression

Solution

Feb 29

Lecture 14 Feature Engineering

Note 14

Lecture Participation 14 Lecture Participation 13

Exam Prep 5 OLS

Solution

Mar 1

Lab 6 OLS (due Mar 5)

Week 8

Mar 5

Lecture 15 Case Study (HCE): CCAO

Discussion 7 Midterm Review

Mar 7

Lecture 16 No Lecture

Midterm Exam Midterm (7-9 PM PST)

Mar 8

Lab 7 Gradient descent and Sklearn (due Mar 12)

Project A1 Housing I (due Mar 14)

Week 9

Mar 12

Lecture 17 Cross-Validation and Regularization

Discussion 8 Feature Engineering, Housing

Mar 14

Lecture 18 Random Variables

Mar 15

Lab 8 Model Selection (due Mar 19)

Project A2 Housing II (due Mar 21)

Week 10

Mar 19

Lecture 19 Estimators, Bias, and Variance

Discussion 9 Cross-Validation and Regularization

Mar 21

Lecture 20 Causal Inference and Confounding

Mar 22

Lab 9 Probability (due Apr 2)

Homework 6 Probability (due Apr 4)

Week 11

Mar 25

Spring Break

Mar 26

Spring Break

Mar 27

Spring Break

Mar 28

Spring Break

Mar 29

Spring Break

Week 12

Apr 2

Lecture 21 SQL I

Discussion 10 RVs, Bias, and Variance

Apr 4

Lecture 22 SQL II and Cloud Data

Apr 5

Lab 10 SQL (due Apr 9)

Homework 7 SQL (due Apr 11)

Week 13

Apr 9

Lecture 23 Logistic Regression I

Discussion 11 SQL

Apr 11

Lecture 24 Logistic Regression II

Apr 12

Lab 11 Logistic Regression (due Apr 16)

Project B1 Spam & Ham I (due Apr 18)

Week 14

Apr 16

Lecture 25 PCA I

Discussion 12 Logistic Regression

Apr 18

Lecture 26 PCA II

Apr 19

Lab 12 PCA (due Apr 23)

Project B2 Spam & Ham II (due Apr 25)

Week 15

Apr 23

Lecture 27 KMeans Clustering

Discussion 13 PCA

Apr 25

Lecture 28 Guest + closing

Apr 26

Lab 13 Clustering (due Apr 30)

Week 16

Apr 29

RRR

Apr 30

RRR

May 1

RRR

May 2

RRR

May 3

RRR

Week 17

May 9

Final Exam Final (8-11 AM PST)