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 Cross-Validation 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 Cross-Validation 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)