Data 100: Principles and Techniques of Data Science

UC Berkeley, Summer 2023

Ed Datahub Gradescope Extenuating Circumstances

Bella Crouch

She/Her/Hers

isabella.crouch@berkeley.edu

Office Hours: Tue, Th 3-4pm (Warren 111)

Dominic Liu

He/Him/His

hangxingliu@berkeley.edu

Office Hours: Mon, Wed 3-4pm (Warren 111)

Welcome to Week 8!

Schedule

Week 1

Jun 20

Lecture 1 Course Overview

Note 1

Lab 1 Prerequisite Coding (due Jun 24)

Homework 1A Plotting and the Permutation Test (due Jun 26)

Homework 1B Prerequisite Math (due Jun 26)

Jun 21

Lecture 2 Pandas I

Note 2

Discussion 1 Math Prerequisites

Solution

Jun 22

Lecture 3 Pandas II

Note 3

Week 2

Jun 26

Lecture 4 Pandas III, EDA I

Note 4

Discussion 2 Pandas worksheet, worksheet notebook, groupwork notebook

Solution

Lab 2 Pandas (due Jul 1)

Lab 3 Data Cleaning and EDA (due Jul 1)

Homework 2 Pandas (due Jun 29)

Jun 27

Lecture 5 EDA II

Note 5

Jun 28

Lecture 6 Text Wrangling, Regex

Note 6

Discussion 3 EDA

Solution

Jun 29

Lecture 7 Visualization

Note 7

Homework 3 Tweets (due Jul 3)

Jun 30

Exam Prep 1 Pandas

Solution

Week 3

Jul 3

Break (no lecture)

Discussion 4 Regex (optional)

Solution, Video Walkthrough

Lab 4 Transformation (due Jul 8)

Lab 5 Modeling, Summary Statistics, Loss Functions (due Jul 8)

Homework 4 Bike Sharing (Visualization) (due Jul 6)

Jul 4

Independence Day (no lecture)

Jul 5

Lecture 8 Sampling

Note 8

Discussion 5 Visualization Worksheet, Notebook

Solution

Jul 6

Lecture 9 Modeling, SLR

Note 9

Homework 5A Sampling (due Jul 10)

Homework 5B Modeling (due Jul 10)

Jul 7

Exam Prep 2 Regex, KDE Plots

Solution

Week 4

Jul 10

Lecture 10 Constant model, loss, and transformations

Note 10

Discussion 6 Sampling, SLR

Solution

Lab 6 Ordinary Least Squares (due Jul 15)

Lab 7 Gradient Descent, Feature Engineering (due Jul 15)

Homework 6 Regression (due Jul 13)

Jul 11

Lecture 11 Ordinary Least Squares (Multiple Linear Regression)

Note 11

Jul 12

Lecture 12 Gradient Descent

Note 12

Discussion 7 Transformations, OLS

Solution

Jul 13

Lecture 13 Sklearn, Feature Engineering

Note 13

Project A1 Housing I (due Jul 17)

Week 5

Jul 17

Lecture 14 Case Study in Human Contexts and Ethics (CCAO)

Note 14

Discussion 8 Gradient Descent, Feature Engineering

Solution

Lab 8 Model Selection (due Jul 22)

Project A2 Housing II (due Jul 24)

Jul 18

Lecture 15 Cross-Validation, Regularization

Note 15

Jul 19

Break (no lecture)

Discussion 9 Exam Review

Jul 20

Midterm Midterm Exam (5-7 PM)

Week 6

Jul 24

Lecture 16 Random Variables

Note 16

Discussion 10 Cross-Validation, Regularization

Solution

Lab 9 Probability (due Jul 29)

Lab 10 Logistic Regression (due Jul 29)

Homework 7 Probability and Estimators (due Jul 27)

Jul 25

Lecture 17 Estimators, Bias, and Variance

Note 17

Jul 26

Lecture 18 Logistic Regression I

Note 18

Discussion 11 Random Variables, BVT

Solution

Jul 27

Lecture 19 Logistic Regression II

Note 19

Project B1 Spam & Ham I (due Jul 31)

Jul 28

Exam Prep 3 Regularization, Bias-Variance Tradeoff, Cross-Validation, Random Variables

Solution

Week 7

Jul 31

Lecture 20 SQL I

Note 20

Discussion 12 Logistic Regression

Solution

Lab 11 SQL (due Aug 5)

Lab 12 PCA (due Aug 5)

Project B2 Spam & Ham II (due Aug 3)

Aug 1

Lecture 21 SQL II

Note 21

Aug 2

Lecture 22 PCA I

Note 22

Discussion 13 SQL

Solution

Aug 3

Lecture 23 PCA II

Note 23

Homework 8 SQL, PCA (due Aug 7)

Week 8

Aug 7

Lecture 24 Decision Trees

Note 24

Discussion 14 PCA

Solution

Lab 13 Decision Trees (optional)

Aug 8

Lecture 25 Conclusion

Aug 9

Break (no lecture)

Discussion 15 Decision Trees, Final Review

Aug 10

Final Final Exam (5-7 PM)