Data 100: Principles and Techniques of Data Science
UC Berkeley, Summer 2024
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Schedule
Week 1
- June 17
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- Lecture 1 Course Overview
- Note 1
- Lab 1 Prerequisite Coding, Plotting, and Permutation (due 6/20)
- June 18
- Homework 1A Plotting and Permutation Tests (due 6/20)
- Homework 1B Prerequisite Math (due 6/20)
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- Discussion 1 Prerequisites (virtual walkthrough only)
- Solutions
- June 19
- Juneteenth
- June 20
- Lab 2 Pandas (due 6/23)
- June 21
-
- Lecture 4 Pandas III
- Note 4
- Homework 2 Food Safety I (due 6/24)
Week 2
- June 24
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- Lecture 5 Data Wrangling and EDA I
- Note 5
- Lab 3 Data Wrangling and EDA (due 6/26)
- June 25
-
- Lecture 6 Text Wrangling and Regex
- Note 6
- Homework 3 Food Safety II (due 6/27)
- June 26
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- Discussion 3 Regex and EDA
- Solutions
- June 27
-
- Lecture 7 Visualization I
- Note 7
- Lab 4 Regex and EDA (due 6/30)
- June 28
-
- Lecture 8 Visualization II (Guest: Jun Yuan)
- Note 8
- Homework 4 Text Analysis of Bloomberg Articles (due 7/1)
Week 3
- July 1
- Lab 5 Transformations (due 7/3)
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- Discussion 4 Visualization and Transformation
- Solutions
- July 2
-
- Lecture 10 Modeling and SLR
- Note 10
- Homework 5 Bike Sharing (due 7/4)
- July 3
- No Discussion
- July 4
- No Lecture
- Homework 6A Sampling (due 7/8)
- Homework 6B Modeling (due 7/8)
- July 5
- No Lecture
Week 4
- July 8
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- Lecture 11 Constant Model, Loss, and Transformations
- Note 11
- Lab 6 Modeling, Loss Functions, and Summary Statistics (due 7/10)
- July 9
-
- Lecture 12 OLS (Multiple Regression)
- Note 12
- Homework 7 Regression (due 7/11)
- July 10
- July 11
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- Lecture 13 Gradient Descent and sklearn
- Note 13
- Lab 7 Ordinary Least Squares (due 7/14)
- July 12
-
- Lecture 14 Feature Engineering
- Note 14
- Project A1 Housing I (due 7/16)
Week 5
- July 15
- Lab 8 Gradient Descent and sklearn (due 7/17)
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- Discussion 7 Gradient Descent and Feature Engineering
- Solutions
- July 16
-
- Lecture 16 HCE Case Study: CCAO (Prerecorded)
- Note 16
- Project A2 Housing II (due 7/22)
- July 17
-
- Discussion 8 Exam Review
- Solutions
- July 18
-
- Lecture 17 Random Variables
- Note 17
- Lab 9 Model Selection, Regularization, and Cross-Validation (due 7/21)
- July 19
- Midterm Midterm
Week 6
- July 22
-
- Lecture 18 Estimators, Bias, and Variance
- Note 18
- Lab 10 Probability (due 7/24)
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- Discussion 9 Cross-Validation and Regularization
- Solutions
- July 23
-
- Lecture 19 Parameter Inference and Bootstrapping
- Note 19
- Homework 8 Probability and Estimators (due 7/25)
- July 24
-
- Discussion 10 Random Variables, Bias, and Variance
- Solutions
- July 25
- Lab 11 SQL (due 7/28)
- July 26
-
- Lecture 21 Logistic Regression I
- Note 22
- Homework 9 SQL (due 7/29)
Week 7
- July 29
-
- Lecture 22 Logistic Regression II
- Note 23
- Lab 12 Logistic Regression (due 7/31)
- Project B1 Spam and Ham I (due 8/1)
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- Discussion 11 SQL (Worksheet Notebook)
- Solutions
- July 30
-
- Lecture 23 Decision Trees
- Note 27
- July 31
-
- Discussion 12 Logistic Regression
- Solutions
- August 1
- Lab 13 PCA (due 8/4)
- Project B2 Spam and Ham II (due 8/5)
- August 2
-
- Lecture 25 Clustering
- Note 26
- Homework 10 PCA and Decision Trees (Extra Credit) (due 8/5)
Week 8
- August 5
- Lecture 26 Intro to Diffusion Models & Conclusion
- Lab 14 Clustering (due 8/7)
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- Discussion 13 PCA and Clustering
- Solutions
- August 6
- Lecture 27 Industry/Academia Roundtable Discussion: Pioneering Pathways: Data Science, AI and Career
- August 7
- Discussion 14 Final Review
- August 8
- Final Exam Final