diff --git a/_site/index.html b/_site/index.html index 82f57e1..35aee78 100644 --- a/_site/index.html +++ b/_site/index.html @@ -1 +1 @@ - Home / Schedule | Data 100 Skip to main content Link Menu Expand (external link) Document Search Copy Copied

Data 100: Principles and Techniques of Data Science

UC Berkeley, Summer 2024

Ed Datahub Gradescope Lectures Playlist Emergency Accommodations Office Hours Queue

Kevin Miao

Kevin Miao

He/Him/His

kevinmiao@berkeley.edu

Office Hours: Mondays & Fridays from 11:00AM to 12:00PM in Evans 455

Maya Shen

Maya Shen

She/Her/Hers

mayashen@berkeley.edu

Office Hours: Tuesdays & Thursdays 11:00AM to 12:00PM in Evans 455

Schedule

Week 1

June 17
Lecture 1 Course Overview
Note 1
Lab 1 Prerequisite Coding, Plotting, and Permutation (due 6/20)
June 18
Lecture 2 Pandas I
Note 2
Homework 1A Plotting and Permutation Tests (due 6/20)
Homework 1B Prerequisite Math (due 6/20)
Discussion 1 Prerequisites (virtual walkthrough only)
Solutions
June 19
Juneteenth
June 20
Lecture 3 Pandas II
Note 3
Lab 2 Pandas (due 6/23)
June 21
Lecture 4 Pandas III
Note 4
Homework 2 Food Safety I (due 6/24)

Week 2

Week 3

July 1
Lecture 9 Sampling
Note 9
Lab 5 Transformations (due 7/3)
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
Lecture 11 Constant Model, Loss, and Transformations
Note 11
Lab 6 Modeling, Loss Functions, and Summary Statistics (due 7/10)
Discussion 5 Probability, Sampling, and Simple Linear Regression
July 9
Lecture 12 OLS (Multiple Regression)
Note 12
Homework 7 Regression (due 7/11)
July 10
Discussion 6 Constant Models, OLS, and Multiple Linear Regression
July 11
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 7/15)

Week 5

July 15
Lecture 15 Cross-Validation and Regularization
Note 15
Lab 8 Gradient Descent and Feature Engineering (due 7/17)
Project A2 Housing II (due 7/18)
Discussion 7 Gradient Descent and Feature Engineering
July 16
Lecture 16 TBD
July 17
Discussion 8 Exam Review
July 18
Lecture 17 Case Study (HCE): CCAO
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 7/10)
Discussion 9 Cross-Validation and Regularization
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
July 25
Lecture 20 SQL
Note 20
Lab 11 SQL (due 7/28)
July 26
Lecture 21 Logistic Regression I
Note 21
Homework 9 SQL (due 7/29)

Week 7

July 29
Lecture 22 Logistic Regression II
Note 22
Lab 12 Logistic Regression (due 7/31)
Project B1 Spam and Ham I (due 8/1)
Discussion 11 SQL
July 30
Lecture 23 Ensembles
July 31
Discussion 12 Logistic Regression
August 1
Lecture 24 PCA
Note 24
Lab 13 PCA (due 8/4)
August 2
Lecture 25 Clustering
Note 25
Project B2 Spam and Ham II (due 8/5)
Homework 10 PCA and Clustering (due 8/5)

Week 8

August 5
Lecture 26 Conclusion
Lab 14 Clustering (due 8/7)
Discussion 13 PCA and Clustering
August 6
Lecture 27 Guest Lecture
August 7
Discussion 14 Final Review
August 8
Final Exam Final
+ Home / Schedule | Data 100 Skip to main content Link Menu Expand (external link) Document Search Copy Copied

Data 100: Principles and Techniques of Data Science

UC Berkeley, Summer 2024

Ed Datahub Gradescope Lectures Playlist Emergency Accommodations Office Hours Queue

Kevin Miao

Kevin Miao

He/Him/His

kevinmiao@berkeley.edu

Office Hours: Mondays & Fridays from 11:00AM to 12:00PM in Evans 455

Maya Shen

Maya Shen

She/Her/Hers

mayashen@berkeley.edu

Office Hours: Tuesdays & Thursdays 11:00AM to 12:00PM in Evans 455

Schedule

Week 1

June 17
Lecture 1 Course Overview
Note 1
Lab 1 Prerequisite Coding, Plotting, and Permutation (due 6/20)
June 18
Lecture 2 Pandas I
Note 2
Homework 1A Plotting and Permutation Tests (due 6/20)
Homework 1B Prerequisite Math (due 6/20)
Discussion 1 Prerequisites (virtual walkthrough only)
Solutions
June 19
Juneteenth
June 20
Lecture 3 Pandas II
Note 3
Lab 2 Pandas (due 6/23)
June 21
Lecture 4 Pandas III
Note 4
Homework 2 Food Safety I (due 6/24)

Week 2

Week 3

July 1
Lecture 9 Sampling
Note 9
Lab 5 Transformations (due 7/3)
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
Lecture 11 Constant Model, Loss, and Transformations
Note 11
Lab 6 Modeling, Loss Functions, and Summary Statistics (due 7/10)
Discussion 5 Probability, Sampling, and Simple Linear Regression
July 9
Lecture 12 OLS (Multiple Regression)
Note 12
Homework 7 Regression (due 7/11)
July 10
Discussion 6 Constant Models, OLS, and Multiple Linear Regression
July 11
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 7/15)

Week 5

July 15
Lecture 15 Cross-Validation and Regularization
Note 15
Lab 8 Gradient Descent and Feature Engineering (due 7/17)
Project A2 Housing II (due 7/18)
Discussion 7 Gradient Descent and Feature Engineering
July 16
Lecture 16 TBD
July 17
Discussion 8 Exam Review
July 18
Lecture 17 Case Study (HCE): CCAO
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 7/10)
Discussion 9 Cross-Validation and Regularization
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
July 25
Lecture 20 SQL
Note 20
Lab 11 SQL (due 7/28)
July 26
Lecture 21 Logistic Regression I
Note 21
Homework 9 SQL (due 7/29)

Week 7

July 29
Lecture 22 Logistic Regression II
Note 22
Lab 12 Logistic Regression (due 7/31)
Project B1 Spam and Ham I (due 8/1)
Discussion 11 SQL
July 30
Lecture 23 Ensembles
July 31
Discussion 12 Logistic Regression
August 1
Lecture 24 PCA
Note 24
Lab 13 PCA (due 8/4)
August 2
Lecture 25 Clustering
Note 25
Project B2 Spam and Ham II (due 8/5)
Homework 10 PCA and Clustering (due 8/5)

Week 8

August 5
Lecture 26 Conclusion
Lab 14 Clustering (due 8/7)
Discussion 13 PCA and Clustering
August 6
Lecture 27 Guest Lecture
August 7
Discussion 14 Final Review
August 8
Final Exam Final
diff --git a/index.md b/index.md index f7039ec..690835f 100644 --- a/index.md +++ b/index.md @@ -17,7 +17,7 @@ UC Berkeley, Summer 2024 [Ed](https://edstem.org/us/courses/59974/){:target="\_blank" .btn .btn-ed .mr-1 } [Datahub](http://data100.datahub.berkeley.edu/){:target="\_blank" .btn .btn-datahub .mr-1 } [Gradescope](https://www.gradescope.com/courses/788548){:target="\_blank" .btn .btn-gradescope .mr-1 } -[Lectures Playlist](https://www.youtube.com/playlist?list=PLQCcNQgUcDfrWFdpg8ww0zx6Q4lKsQkjc&playnext=1&index=1){:target="\_blank" .btn .btn-youtube .mr-1} +[Lectures Playlist](https://bcourses.berkeley.edu/courses/1535115/external_tools/90481){:target="\_blank" .btn .btn-youtube .mr-1} [Emergency Accommodations](https://docs.google.com/forms/d/e/1FAIpQLSe637FZSvtd0zRtvs3jsTvHojF2HH90D_YR84YWIaRAaNxc5w/viewform){:target="\_blank" .btn .btn-blue .mr-1 } [Office Hours Queue](https://oh.ds100.org/){:target="\_blank" .btn .btn-oh .mr-1}