├── _sass ├── color_schemes │ └── wider.scss └── custom │ ├── announcement.scss │ ├── variables.scss │ ├── staffer.scss │ ├── card.scss │ ├── schedule.scss │ ├── module.scss │ └── custom.scss ├── favicon.ico ├── assets ├── staff │ ├── aidan.jpg │ ├── alex.jpg │ ├── boyu.jpg │ ├── derry.jpg │ ├── jacob.jpg │ ├── lauren.jpg │ ├── ramesh.jpg │ ├── sandya.jpg │ ├── will.jpg │ └── charisse.jpg ├── brand │ └── favicon.png ├── exams │ ├── final │ │ ├── fa19_final.pdf │ │ ├── fa20_final.pdf │ │ ├── fa19_final_sol.pdf │ │ └── fa20_final_sol.pdf │ ├── midterm_1 │ │ ├── fa19_mt.pdf │ │ ├── fa20_mt.pdf │ │ ├── fa21_mt1.pdf │ │ ├── fa22_mt1.pdf │ │ ├── fa23_mt1.pdf │ │ ├── fa24_mt1.pdf │ │ ├── sp20_mt.pdf │ │ ├── sp21_mt1.pdf │ │ ├── sp22_mt1.pdf │ │ ├── sp23_mt1.pdf │ │ ├── sp24_mt1.pdf │ │ ├── fa19_mt_sol.pdf │ │ ├── fa20_mt_sol.pdf │ │ ├── sp20_mt_sol.pdf │ │ ├── fa21_mt1_sol.pdf │ │ ├── fa22_mt1_sol.pdf │ │ ├── fa23_mt1_sol.pdf │ │ ├── fa24_mt1_sol.pdf │ │ ├── sp21_mt1_sol.pdf │ │ ├── sp22_mt1_sol.pdf │ │ ├── sp23_mt1_sol.pdf │ │ └── sp24_mt1_sol.pdf │ └── midterm_2 │ │ ├── fa21_mt2.pdf │ │ ├── fa22_mt2.pdf │ │ ├── fa23_mt2.pdf │ │ ├── fa24_mt2.pdf │ │ ├── sp21_mt2.pdf │ │ ├── sp22_mt2.pdf │ │ ├── sp23_mt2.pdf │ │ ├── sp24_mt2.pdf │ │ ├── fa21_mt2_sol.pdf │ │ ├── fa22_mt2_sol.pdf │ │ ├── fa23_mt2_sol.pdf │ │ ├── fa24_mt2_sol.pdf │ │ ├── sp21_mt2_sol.pdf │ │ ├── sp22_mt2_sol.pdf │ │ ├── sp23_mt2_sol.pdf │ │ ├── sp24_mt2_sol.pdf │ │ └── fa22_mt2_sol_not_complete.pdf └── references │ ├── midterm_1_reference.pdf │ └── midterm_2_reference.pdf ├── Gemfile ├── _layouts ├── module.html ├── mathjax.html ├── announcement.html ├── staffer.html └── schedule.html ├── _staffers ├── ramesh.md ├── alex.md ├── boyu.md ├── derry.md ├── jacob.md ├── will.md ├── aidan.md ├── charisse.md ├── lauren.md └── sandya.md ├── _includes ├── head_custom.html ├── mathjax.html └── minutes.liquid ├── _modules ├── week-01.md ├── week-15.md ├── week-12.md ├── week-13.md ├── week-06.md ├── week-05.md ├── week-08.md ├── week-10.md ├── week-03.md ├── week-07.md ├── week-14.md ├── week-11.md ├── week-02.md ├── week-09.md └── week-04.md ├── lecture ├── lec23.md ├── lec26.md ├── lec24.md ├── lec25.md ├── lec22.md ├── lec18.md ├── lec10.md ├── lec12.md ├── lec01.md ├── lec15.md ├── lec11.md ├── lec04.md ├── lec17.md ├── lec20.md ├── lec05.md ├── lec21.md ├── lec02.md ├── lec06.md ├── lec19.md ├── lec16.md ├── lec03.md ├── lec08.md ├── lec13.md ├── lec09.md ├── lec14.md └── lec07.md ├── index.md ├── lecture_names.txt ├── LICENSE ├── .gitignore ├── calendar.md ├── staff.md ├── initial_setup.py ├── README.md ├── .github └── workflows │ └── jekyll.yml ├── _config.yml ├── Gemfile.lock ├── resources.md └── syllabus.md /_sass/color_schemes/wider.scss: -------------------------------------------------------------------------------- 1 | $content-width: 1200px; -------------------------------------------------------------------------------- /favicon.ico: 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-------------------------------------------------------------------------------- /_staffers/ramesh.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Ramesh Sridharan 3 | role: Instructor 4 | email: ramesh_s 5 | website: 6 | photo: ramesh.jpg 7 | pronouns: 8 | --- -------------------------------------------------------------------------------- /assets/exams/midterm_2/fa22_mt2_sol_not_complete.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ds-102/fa24/main/assets/exams/midterm_2/fa22_mt2_sol_not_complete.pdf -------------------------------------------------------------------------------- /_staffers/alex.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Alexander Strang 3 | role: Instructor 4 | email: alexstrang 5 | website: http://alexanderstrang.com/ 6 | photo: alex.jpg 7 | pronouns: 8 | --- -------------------------------------------------------------------------------- /_includes/head_custom.html: -------------------------------------------------------------------------------- 1 | {% assign math = page.math | default: layout.math | default: site.math %} 2 | 3 | {% case math %} 4 | {% when "mathjax" %} 5 | {% include mathjax.html %} 6 | {% endcase %} -------------------------------------------------------------------------------- /_sass/custom/announcement.scss: -------------------------------------------------------------------------------- 1 | .announcement { 2 | @extend %card; 3 | 4 | h1, h2 { 5 | @extend .text-gamma; 6 | } 7 | 8 | .announcement-meta { 9 | @extend .text-epsilon; 10 | } 11 | } 12 | -------------------------------------------------------------------------------- /_staffers/boyu.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Boyu Fan 3 | team: Staff 4 | role: TA 5 | email: david_fan1@berkeley.edu 6 | website: 7 | photo: boyu.jpg 8 | pronouns: 9 | --- 10 | 11 | Hi all! I’m Boyu, a rising senior studying Data Science, Statistics, and Economics. Hope you have fun in Data 102! -------------------------------------------------------------------------------- /_layouts/mathjax.html: -------------------------------------------------------------------------------- 1 | --- 2 | layout: default 3 | math: mathjax 4 | --- 5 |
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-------------------------------------------------------------------------------- /_staffers/derry.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Derry Xu 3 | team: Staff 4 | role: TA 5 | email: derryxu@berkeley.edu@berkeley.edu 6 | website: 7 | photo: derry.jpg 8 | pronouns: 9 | --- 10 | 11 | Hi everyone, my name is Derry. I'm a senior studying CS/DS/Stats, looking forward to meeting everyone this semester! 12 | -------------------------------------------------------------------------------- /_modules/week-01.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 1: Introductions 3 | ref: 'week-1' 4 | --- 5 | 6 | Aug 29 7 | : **Lecture**{: .label .label-lecture } [1. Binary Decision-Making I](lecture/lec01) 8 | : **Vitamin**{: .label .label-vit } [Vitamin 1](https://www.gradescope.com/courses/845267/assignments/4844740) (due Sep 4 at 11:59 PM) 9 | -------------------------------------------------------------------------------- /_modules/week-15.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 15: Wrapup 3 | ref: 'week-15' 4 | --- 5 | 6 | Dec 3 7 | : **Lecture**{: .label .label-lecture } [25. Bridging Technical & Ethical Perspectives on Modeling and Decisions](lecture/lec25) 8 | 9 | Dec 5 10 | : **Lecture**{: .label .label-lecture } [26. Course Wrap-Up](lecture/lec26) 11 | -------------------------------------------------------------------------------- /_staffers/jacob.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Jacob Taegon Kim 3 | team: Staff 4 | role: Tutor 5 | email: jacobkim@berkeley.edu 6 | website: 7 | photo: jacob.jpg 8 | pronouns: 9 | --- 10 | 11 | Hi! I’m Jacob, a senior from LA studying CS and DS. In my free time, I enjoy running and making coffee. Feel free to reach out to me for anything! -------------------------------------------------------------------------------- /_staffers/will.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Will Rathgeb 3 | team: Staff 4 | role: Tutor 5 | email: willrathgeb@berkeley.edu 6 | website: 7 | photo: will.jpg 8 | pronouns: He/Him/His 9 | --- 10 | 11 | Hey everyone! I’m Will, a senior studying DS and Stats. In my free time I like to watch sports, workout, and play tennis. I hope you all enjoy 102! -------------------------------------------------------------------------------- /_staffers/aidan.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Aidan McLoughlin 3 | team: Staff 4 | role: TA 5 | email: aidan_mcloughlin@berkeley.edu 6 | website: 7 | photo: aidan.jpg 8 | pronouns: He/Him/His 9 | --- 10 | 11 | Hello! I'm Aidan, a PhD student in the Biostatistics program. I enjoy hiking, climbing and chess. Excited to work with you all this semester! -------------------------------------------------------------------------------- /_staffers/charisse.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Charisse Liu 3 | team: Staff 4 | role: TA 5 | email: charisseliu@berkeley.edu 6 | website: 7 | photo: charisse.jpg 8 | pronouns: 9 | --- 10 | 11 | Hi! I’m a senior studying DS & Stats. I like crosswords, arts & crafts, and Freshroll. Excited to be a part of your Data 102 journey, and feel free to reach out for anything! -------------------------------------------------------------------------------- /_staffers/lauren.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Lauren Lee 3 | team: Staff 4 | role: Tutor 5 | email: laulee@berkeley.edu 6 | website: 7 | photo: lauren.jpg 8 | pronouns: 9 | --- 10 | 11 | Hi y’all, my name is Lauren and I’m a senior studying data science. I am addicted to sudoku and like to solve the ones on the NYT. I’m excited to meet everyone in one of my favorite classes here at Berkeley! -------------------------------------------------------------------------------- /lecture/lec23.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 23 - MCTS 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 23 - MCTS 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/11VRkDz4CMn7SAD0wb_Xao5WZih97Zi648g3Db-fvyOg/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-23-mcts) 13 | -------------------------------------------------------------------------------- /lecture/lec26.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 26 - Course Wrap-Up 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 26 - Course Wrap-Up 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1B7zLMEhCf-EhNzrytRaPxTqtcHLr-pOtlpJ9oMZlhok/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-26-course-wrap-up) 13 | -------------------------------------------------------------------------------- /lecture/lec24.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 24 - Case Studies 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 24 - Case Studies, Robustness, and Generalization 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1aJ0ow_NciZmOnsr5fOMmlJXsj1vHOy-fJe1wx6s-Xso/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-24-case-studies) 13 | -------------------------------------------------------------------------------- /_includes/mathjax.html: -------------------------------------------------------------------------------- 1 | 2 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /_staffers/sandya.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Sandya Wijaya 3 | team: Staff 4 | role: TA 5 | email: sandya.wijaya@berkeley.edu 6 | website: 7 | photo: sandya.jpg 8 | pronouns: 9 | --- 10 | 11 | Hi! I'm Sandya, an IEOR Masters student from Indonesia. In my free time I love to read & explore new food and nature spots. I'm passionate about the intersection of tech & climate, and Data 102 has played a pivotal role in me navigating my path -- I hope this class will leave a positive impact on you too :D -------------------------------------------------------------------------------- /_sass/custom/variables.scss: -------------------------------------------------------------------------------- 1 | // Typography 2 | $body-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif; 3 | 4 | // Colors 5 | $link-color: $blue-100; 6 | 7 | // Layout 8 | $gutter-spacing: $sp-6; 9 | $gutter-spacing-sm: $sp-4; 10 | $nav-width: 200px; 11 | $content-width: 2000px; 12 | 13 | // Components 14 | $box-shadow: 0 1px 3px rgba(0, 0, 0, 0.07), 0 4px 14px rgba(0, 0, 0, 0.05); 15 | $module-date-color: $link-color; 16 | $staffer-image-size: 100px; 17 | -------------------------------------------------------------------------------- /lecture/lec25.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 25 - Bridging Technical and Ethical Perspectives 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 25 - Bridging Technical and Ethical Perspectives 8 | 9 | Presented by Alexander Strang and Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1f3Wsxgd6IO9G5U1WlbwepwJaXfjCyQ0fYLdBqG1Xlhs/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-25-bridging-technical-and-ethical-perspectives) 13 | -------------------------------------------------------------------------------- /lecture/lec22.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 22 - Reinforcement Learning II 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 22 - Reinforcement Learning II 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Notes](https://drive.google.com/file/d/1RNCN2QXTNG2SRq5-oUA_hKCYf-q_39Vg/view?usp=sharing) 12 | - [Slides](https://docs.google.com/presentation/d/1bVC-yw5P0IMmJnQ7arGBuWkCKOhKpPlZuiKPdXGQ6nE/edit?usp=sharing) 13 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-22-reinforcement-learning-ii) 14 | -------------------------------------------------------------------------------- /lecture/lec18.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 18 - Tail Bounds 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 18 - Tail Bounds 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Notes](https://drive.google.com/file/d/1mUTxUwyrJOdgFPamKLLybvIZvz7Knr2p/view?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-18-tail-bounds) 13 | - Textbook, [Martin Wainwright - High Dimensional Statistics - Chapter 4](https://drive.google.com/file/d/1Fna7Qt7MIU9ylNwBbAN8FmpigLzf9zX2/view?usp=sharing) 14 | -------------------------------------------------------------------------------- /lecture/lec10.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 10 - Sampling and Prediction 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 10 - Sampling and Prediction 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1MorXNxAPvRlTbU40oNojqUsPViHiUPnOVSJAoIl4OTQ/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-10-sampling-and-prediction) 13 | - Textbook reading: [Chapter 2.4](https://data102.org/ds-102-book/content/chapters/02/04_inference_sampling.html) 14 | -------------------------------------------------------------------------------- /_modules/week-12.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 12: Reinforcement Learning I 3 | ref: 'week-12' 4 | --- 5 | 6 | Nov 12 7 | : **Lecture**{: .label .label-lecture } [21. Reinforcement Learning I](lecture/lec21) 8 | 9 | Nov 13 10 | : **Review Session**{: .label .label-disc} [Slides](https://docs.google.com/presentation/d/1YX5z_S9IKXn7fW3mfM6ap45_NtxcizBDHFxYgQUpfpY/edit?usp=sharing) 11 | 12 | Nov 14 13 | : **Midterm**{: .label .label-exam } Midterm 2 14 | : **Vitamin**{: .label .label-vit } [Vitamin 11](https://www.gradescope.com/courses/845267/assignments/5326743) (due Nov 17 at 11:59 PM) 15 | -------------------------------------------------------------------------------- /lecture/lec12.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 12 - Model Checking and Uncertainty Quantification 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 12 - Generalized Linear Models 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1J8Xf_yKKD1qYp3QkJUwTREU5rsO284fpcTLsetkdQqg/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-12-model-checking-and-uncertainty-quantification) 13 | - [Notes](https://drive.google.com/file/d/1MvU9UwQ68X_2Zu84h7I30rviTMtKIk39/view?usp=sharing) 14 | 15 | -------------------------------------------------------------------------------- /lecture/lec01.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 1 – Course Overview 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 1 – Introduction 8 | 9 | Presented by Ramesh Sridharan and Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1OAlqkUCsObOBhm6Z2wWYLkcP1sJC5ZQU4n_X7Q4s6do/edit?usp=sharing) 12 | - Optional reading 13 | - [What are the most important statistical ideas of the past 50 years?](https://arxiv.org/pdf/2012.00174){:target="_blank"} 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-1-binary-decision-making-i) 15 | -------------------------------------------------------------------------------- /lecture/lec15.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 15 - Causal Inference I 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 15 - Causal Inference I: Problems with Association 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1dLKPZpQzvJqPIiwhycZshHrONdwHKitU6pJmbQqjxi4/edit?usp=sharing){:target="_blank"} [(Annotated)](https://drive.google.com/file/d/1GPbr2e7Nda6_h9WCqtjjMgB_TukgNINU/view?usp=drive_link) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-15-causal-inference-i) 13 | - Textbook reading: [4.1 - 4.3](https://data102.org/ds-102-book/content/chapters/04/intro.html) 14 | -------------------------------------------------------------------------------- /lecture/lec11.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 11 - Generalized Linear Models 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 11 - Generalized Linear Models 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1suA5mr0lSa03x0kqhGQEJWhbU3B5zQC462O6kRBFawY/edit?usp=sharing) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-11-generalized-linear-models) 13 | - [Demo notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture11%2Fglms_ppc.ipynb&branch=main) 14 | -------------------------------------------------------------------------------- /lecture/lec04.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 4 – Binary Decision Making IV 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 4 – Binary Decision Making IV (Balancing Specificity and Sensitivity) 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1FkTnG4eoDBieIjj2bZLLSerFWbt0xTXhb8jHUT9NS7c/edit?usp=sharing) 12 | - [Notes: Neyman Pearson](https://drive.google.com/file/d/1uih0hyZkwJaAun5iMG1ZDdrFo8sTEZJj/view?usp=sharing) 13 | - [Text Book Chapter 1.3](https://data102.org/ds-102-book/content/chapters/01/03_multiple_tests.html) 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-3-binary-decision-making-iv) -------------------------------------------------------------------------------- /_sass/custom/staffer.scss: -------------------------------------------------------------------------------- 1 | .role { 2 | display: flex; 3 | flex-wrap: wrap; 4 | margin-bottom: $sp-5; 5 | } 6 | 7 | .staffer { 8 | display: flex; 9 | flex-basis: 90%; 10 | padding: $sp-4; 11 | padding-right: $sp-10; 12 | 13 | @include mq(sm) { 14 | flex-basis: 50%; 15 | } 16 | 17 | p { 18 | @extend .staffer-name, .fs-3; 19 | } 20 | 21 | .staffer-pronouns { 22 | @extend .label, .text-grey-dk-100, .bg-grey-lt-200; 23 | text-align: center; 24 | border-radius: 9px; 25 | text-transform: none !important; 26 | } 27 | } 28 | 29 | .staffer-image { 30 | border-radius: 50%; 31 | height: $staffer-image-size; 32 | margin-right: $sp-4; 33 | } 34 | 35 | .staffer-name { 36 | margin: $sp-1; 37 | } -------------------------------------------------------------------------------- /lecture/lec17.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 17 - Causal Inference III - Observational Studies 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 17 - Causal Inference III: Observational Studies 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1GH39wi1gJLewLIsdXNFLRIJi_VuWHxAkUIOJ73xVn7w/edit?usp=sharing){:target="_blank"} [(Annotated)](https://drive.google.com/file/d/1x2yYXBtvznGOWpaFHwSAuHyvglyXtzDP/view?usp=drive_link) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-17-causal-inference-iii-observational-studies) 13 | - Textbook reading, [Section 4.4](https://data102.org/ds-102-book/content/chapters/04/05_observational_studies_unconfoundedness.html) 14 | -------------------------------------------------------------------------------- /lecture/lec20.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 20 - Bandits and Regret 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 20 - Bandits and Regret 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Notes](https://drive.google.com/file/d/14oyQjmq0OBhyKuhzPfHTW8CoVvzL44p9/view?usp=sharing) 12 | - [Slides](https://docs.google.com/presentation/d/10Njcpj4OGfrUIEWXvM7nPp75TQBzNiksIuUsZWkTfdY/edit?usp=sharing) 13 | - [Demo Notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture20%2Fbandits_demo.ipynb&branch=main) 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-20-bandits-and-regret) 15 | 16 | -------------------------------------------------------------------------------- /lecture/lec05.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 5 – Decision Theory 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 5 – Decision Theory (Loss and Risk) 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1H4fWtrzVh-LSnx6pehEYDm41ndcAdk7NFVULpU5wwDA/edit?usp=sharing) 12 | - [Review Slides](https://docs.google.com/presentation/d/1ENw3IXAbJMsSsi5iVufnfo1kiqNl6TvQb-MMPNpAUF4/edit?usp=sharing) 13 | - [Notes: Loss and Risk](https://drive.google.com/file/d/1yH5Jh8p4yV1MeorCOSxl7oT6V1UGbJI1/view?usp=sharing) 14 | - [Text Book Chapter 1.5](https://data102.org/ds-102-book/content/chapters/01/05_decision_theory.html) 15 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-5-decision-theory) 16 | -------------------------------------------------------------------------------- /_sass/custom/card.scss: -------------------------------------------------------------------------------- 1 | @mixin abstract-card() { 2 | box-shadow: 0 1px 3px rgba(0, 0, 0, 0.07), 0 4px 14px rgba(0, 0, 0, 0.05); 3 | margin: $sp-4 (-$gutter-spacing-sm); 4 | 5 | @include mq(md) { 6 | border-radius: $border-radius; 7 | margin: $sp-4 0; 8 | } 9 | } 10 | 11 | %card { 12 | @include abstract-card(); 13 | display: flex; 14 | flex-direction: column; 15 | min-width: 0; 16 | padding: 0 $sp-4; 17 | position: relative; 18 | word-wrap: break-word; 19 | 20 | >:first-child { 21 | border-top: none !important; 22 | } 23 | 24 | >:last-child { 25 | border-bottom: none !important; 26 | } 27 | 28 | .label { 29 | border-radius: $border-radius; 30 | margin-left: 0; 31 | user-select: none; 32 | } 33 | } 34 | -------------------------------------------------------------------------------- /lecture/lec21.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 21 - Reinforcement Learning I 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 21 - Reinforcement Learning I 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Notes](https://drive.google.com/file/d/1a3I6bMYXNPnXuZk1F6UU0LDWLFU8go5e/view?usp=sharing) 12 | - [Slides](https://docs.google.com/presentation/d/1SmAQ4mSmP64MM7IWubTR5x6h5SWjz8tO_XnNK2Pj2iY/edit?usp=sharing) 13 | - [Demo notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture21%2Fdynamic_programming.ipynb&branch=main) 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-21-reinforcement-learning-i) 15 | -------------------------------------------------------------------------------- /lecture/lec02.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 2 – Binary Decision Making I 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 2 – Binary Decision Making II (Error Rates, Probability, and the NHST) 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1KM8XxvG2iYQQg9z6M4j5o2qL0xiy25_P8EqgDnq_eZk/edit?usp=sharing) 12 | - [Live Notes](https://drive.google.com/file/d/19JsvBACarGttF2wG8dj_xVzPlSfUTCcl/view?usp=sharing) 13 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-2-binary-decision-making-ii) 14 | - [Text Book Chapter 1.1](https://data102.org/ds-102-book/content/chapters/01/01_decisions_and_errors.html) 15 | - [Text Book Chapter 1.2](https://data102.org/ds-102-book/content/chapters/01/02_hypothesis_testing.html) 16 | -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Home 4 | nav_order: 1 5 | description: A week-to-week description of the content covered in the course. 6 | --- 7 | 8 | # Data 102: Data, Inference, and Decisions 9 | 10 | {: .mb-2 } 11 | UC Berkeley, Fall 2024 12 | {: .mb-0 .fs-6 .text-grey-dk-000 } 13 | 14 |
15 | {% assign instructors = site.staffers | where: 'role', 'Instructor' %} 16 |
17 | {% for staffer in instructors %} 18 | {{ staffer }} 19 | {% endfor %} 20 |
21 |
22 | 23 | {% assign announcement = site.announcements | where: 'week', site.current_week %} 24 | {{ announcement }} 25 | 26 | 27 | ## Schedule 28 | [**Jump to current week**](#week-{{ site.current_week }}){: .btn } 29 | 30 | {% for module in site.modules %} 31 | {{ module }} 32 | {% endfor %} 33 | -------------------------------------------------------------------------------- /_modules/week-13.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 13: Reinforcement Learning II 3 | ref: 'week-13' 4 | --- 5 | 6 | Nov 17 7 | : **Lab**{: .label .label-lab } [Lab 10: Bandits](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab10%2Flab10.ipynb&branch=main) (due Nov 20 at **5 PM**) 8 | 9 | Nov 19 10 | : **Lecture**{: .label .label-lecture } [22. Reinforcement Learning II](lecture/lec22) 11 | : **Discussion**{: .label .label-disc } [Discussion 10](https://drive.google.com/file/d/14NcahvYjz9Bzs0Y-eV5zJwhj_eV67CJW/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1CKwk7fiSFoeFvDXHhmdalBCz8xQ6wRHm/view?usp=sharing)) 12 | 13 | Nov 21 14 | : **Lecture**{: .label .label-lecture } [23. Monte Carlo Tree Search](lecture/lec23) 15 | -------------------------------------------------------------------------------- /lecture/lec06.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 6 - Parameter Estimation and Inference 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 6 - Parameter Estimation and Inference (Bayesian and Frequentist) 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1oUF-ic4asFIpr9mDlqYHEkxSoBHadFL6S6xUSD2mahY/edit?usp=sharing) 12 | - [Whiteboard notes](https://drive.google.com/file/d/1UXIveCbXfvBnxWYQ-PwvFMVeY7qvCamU/view?usp=sharing) 13 | - [Unit 1 Review Slides](https://docs.google.com/presentation/d/1YNkIn0NaFBIEN17xncPa9ZAJDRfODEVcGpmG1Bcyf48/edit?usp=sharing) 14 | - Textbook reading: [Chapter 2.1](https://data102.org/ds-102-book/content/chapters/02/01_parameter_estimation.html) 15 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-6-parameter-estimation-and-inference) 16 | -------------------------------------------------------------------------------- /lecture/lec19.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 19 - Bandits and Concentration Inequalities 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 19 - Bandits and Concentration Inequalities 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Notes](https://drive.google.com/file/d/1FNQNAXBjXFJ2c8ZZzbRQNtLKoGq5NoW2/view?usp=sharing) 12 | - [Handwritten Notes](https://drive.google.com/file/d/1OYdil-QliBKGhx-TP3ikdrsabginKFC-/view?usp=sharing) 13 | - [Slides](https://docs.google.com/presentation/d/1dcXSTdwhToFzepOyKrw3_Olk5VYmRwPMFVq8Bxj3l-0/edit?usp=sharing) 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-19-bandits-and-concentration-inequalities) 15 | - Textbook, [Martin Wainwright - High Dimensional Statistics - Chapter 4](https://drive.google.com/file/d/1Fna7Qt7MIU9ylNwBbAN8FmpigLzf9zX2/view?usp=sharing) 16 | -------------------------------------------------------------------------------- /_includes/minutes.liquid: -------------------------------------------------------------------------------- 1 | {% capture _minutes_workspace %} 2 | {% comment %} 3 | Return the number of minutes between midnight and the given time string (e.g. '9:30 AM'). 4 | 5 | Parameters: 6 | `time` (string): the time to convert. 7 | {% endcomment %} 8 | 9 | {% assign _time = include.time %} 10 | {% assign _hhmm = _time | split: ' ' | first | split: ':' %} 11 | {% assign _hours = _hhmm | first | to_integer %} 12 | {% assign _minutes = _hhmm | last | to_integer %} 13 | {% assign _ampm = _time | split: ' ' | last | upcase %} 14 | 15 | {% if _ampm == 'AM' and _hours == 12 %} 16 | {% assign _hours = _hours | minus: 12 %} 17 | {% elsif _ampm == 'PM' and _hours != 12 %} 18 | {% assign _hours = _hours | plus: 12 %} 19 | {% endif %} 20 | {% endcapture %}{% assign _minutes_workspace = '' %}{{ _hours | times: 60 | plus: _minutes }} 21 | -------------------------------------------------------------------------------- /lecture/lec16.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 16 - Causal Inference II 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 16 - Causal Inference II: Randomized Experiments 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1_gIwWjlPPtUP9fE8mUM_WhwEd5UIjADyWR630KGE-FI/edit?usp=sharing){:target="_blank"} [(Annotated)](https://drive.google.com/file/d/1COu4EnYXIQwAMLSEQ2mRCoVoxFviVAdP/view?usp=drive_link) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-16-causal-inference-ii) 13 | - Textbook reading: 14 | - [Section 4.3](https://data102.org/ds-102-book/content/chapters/04/03_causality.html) 15 | - [Section 4.4](https://data102.org/ds-102-book/content/chapters/04/04_randomized_experiments.html) 16 | - [Section 4.6](https://data102.org/ds-102-book/content/chapters/04/06_instrumental_variables.html) 17 | -------------------------------------------------------------------------------- /_modules/week-06.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 6: GLMs I 3 | ref: 'week-6' 4 | --- 5 | Sep 30 6 | : **Lab**{: .label .label-lab } [Lab 5: Sampling & GLMs](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab05%2Flab05.ipynb&branch=main) (due Oct 2 at **5 PM**) 7 | 8 | Oct 1 9 | : **Lecture**{: .label .label-lecture } [10. Sampling and Prediction](lecture/lec10) 10 | : **Discussion**{: .label .label-disc } [Discussion 5](https://drive.google.com/file/d/1Soq_puPTDdTmZReBNwkVPBxh1m-lG6tH/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1P1qPBhOqtC5PPC3CaykESrS94WaZurov/view?usp=sharing)) 11 | 12 | Oct 3 13 | : **Lecture**{: .label .label-lecture } [11. GLMs](lecture/lec11) 14 | : **Vitamin**{: .label .label-vit } [Vitamin 6](https://www.gradescope.com/courses/845267/assignments/5083124) (due Oct 6 at 11:59 PM) 15 | -------------------------------------------------------------------------------- /lecture/lec03.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 3 – Binary Decision Making III 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 3 – Binary Decision Making III (Testing Single and Multiple Hypotheses) 8 | 9 | Presented by Alexander Strang 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1Jf4kVysMCPa1kLloC8tYx51IZkCO3b95LaOOApEjlYA/edit?usp=sharing) 12 | - [Notes: Significance, Power, and Replicability](https://drive.google.com/file/d/1VQh8eXsNhj9cAjXMsOexbikbOZrgaS4t/view?usp=sharing) 13 | - [Notes: Bonferroni](https://drive.google.com/file/d/1ChH0jF7FfAoRGYwxZYkw3C2RKwiCA5M9/view?usp=sharing) 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-3-binary-decision-making-iii) 15 | - [Text Book Chapter 1.2](https://data102.org/ds-102-book/content/chapters/01/02_hypothesis_testing.html) 16 | - [Text Book Chapter 1.3](https://data102.org/ds-102-book/content/chapters/01/03_multiple_tests.html) 17 | -------------------------------------------------------------------------------- /_modules/week-05.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 5: Bayesian Modeling II 3 | ref: 'week-5' 4 | --- 5 | Sep 23 6 | : **Lab**{: .label .label-lab } [Lab 4: Graphical Models](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab04%2Flab04.ipynb&branch=main) (due Sep 25 at **5 PM**) 7 | 8 | Sep 24 9 | : **Lecture**{: .label .label-lecture } [8. Bayesian Hierarchical Models II](lecture/lec08) 10 | : **Discussion**{: .label .label-disc } [Discussion 4](https://drive.google.com/file/d/11C4p_L4XhOfSJw0GtrIYLmYRnFptOiii/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1zOTqxldN_Qozuv8hMDsSVJP-YGmsh8Dl/view?usp=sharing)) 11 | 12 | Sep 26 13 | : **Lecture**{: .label .label-lecture } [9. Bayesian Inference with Sampling](lecture/lec09) 14 | : **Vitamin**{: .label .label-vit } [Vitamin 5](https://www.gradescope.com/courses/845267/assignments/5031070) (due Sep 29 at 11:59 PM) 15 | -------------------------------------------------------------------------------- /lecture/lec08.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 8 - Bayesian Hierarchical Models II 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 8 - Bayesian Hierarchical Models II 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1u1mzKpPqMmehjlcv_Z_nOCk_A2E1vCh8JLc6N8jsLLg/edit?usp=sharing) ([Annotated](https://drive.google.com/file/d/1Xxd4koXQtWUGSgr0fKYLgdOABiOBnAZ8/view?usp=drive_link)) 12 | - [Demo notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture08%2Flec08.ipynb&branch=main) 13 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-8-bayesian-hierarchical-models-ii) 14 | - Textbook reading: 15 | - [Chapter 2.2](https://data102.org/ds-102-book/content/chapters/02/02_hierarchical_models.html) 16 | - [Chapter 2.3](https://data102.org/ds-102-book/content/chapters/02/03_graphical_models.html) 17 | -------------------------------------------------------------------------------- /_modules/week-08.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 8: Nonparametric Methods 3 | ref: 'week-8' 4 | --- 5 | Oct 14 6 | : **Lab**{: .label .label-lab } [Lab 6: GLMs and the Bootstrap](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab06%2Flab06.ipynb&branch=main) (due Oct 16 at **5 PM**) 7 | 8 | Oct 15 9 | : **Lecture**{: .label .label-lecture } [13. Nonparametric Methods and Neural Networks](lecture/lec13) 10 | : **Discussion**{: .label .label-disc } [Discussion 6](https://drive.google.com/file/d/1LGXjyweih1bn33w_ita4118IymVhnao4/view?usp=drive_link) ([Answers](https://drive.google.com/file/d/1tpdGF307_UzCI2hWxiRaPma8UMRjv6e3/view?usp=drive_link)) 11 | 12 | Oct 17 13 | : **Lecture**{: .label .label-lecture } [14. Neural Networks and Interpretability](lecture/lec14) 14 | : **Vitamin**{: .label .label-vit } [Vitamin 7](https://www.gradescope.com/courses/845267/assignments/5165756) (due Oct 20 at 11:59 PM) 15 | -------------------------------------------------------------------------------- /_modules/week-10.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 10: Causal Inference II 3 | ref: 'week-10' 4 | --- 5 | Oct 28 6 | : **Lab**{: .label .label-lab } [Lab 8: Instrumental variable](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab08%2Flab08.ipynb&branch=main) (due Oct 30th at **5 PM**) 7 | 8 | Oct 29 9 | : **Lecture**{: .label .label-lecture } [17. Causal Inference III: Observational Studies](lecture/lec17) 10 | : **Discussion**{: .label .label-disc } [Discussion 8](https://drive.google.com/file/d/1EhUOyDejlf26y5p7hJU1fwhrM-eG2psY/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1dJQDk8KjSRLpyPJulhh50TXIfz644XSp/view?usp=sharing)) 11 | 12 | Oct 31 13 | : **Lecture**{: .label .label-lecture } [18. Concentration Inequalities and Tail Bounds](lecture/lec18) 14 | : **Vitamin**{: .label .label-vit } [Vitamin 9](https://www.gradescope.com/courses/845267/assignments/5241780) (due Nov 3 at 11:59 PM) 15 | -------------------------------------------------------------------------------- /lecture/lec13.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 13 - Nonparametric Methods and Interpretability 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 13 - Nonparametric Methods and Interpretability 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1leSBjDu-pCVUzXJuvnrEmDGA-u1OBhTWIheori8NLl0/edit?usp=sharing){:target="_blank"} [(Annotated)](https://drive.google.com/file/d/1flGNOkbS-4olosSecwEKhuxa7GSF9jlM/view?usp=drive_link) 12 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-13-nonparametric-methods-and-interpretability) 13 | - [Demo and typed notes](http://data102.datahub.berkeley.edu/hub/user-redirect/git-sync?repo=https://github.com/ds-102/fa24-materials&subPath=lecture/lecture13/nonparametric_interp.ipynb){:target="_blank"} 14 | - Required reading for next class (lecture 14) - [Statistical Modeling, The Two Cultures, by Leo Breiman](http://cda.psych.uiuc.edu/statistical_learning_course/breiman_two_cultures.pdf) (pp 1-11) 15 | -------------------------------------------------------------------------------- /_modules/week-03.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 3: Decisions II 3 | ref: 'week-3' 4 | --- 5 | 6 | Sep 9 7 | : **Lab**{: .label .label-lab } [Lab 2: Testing](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab02%2Flab02.ipynb&branch=main) (due Sep 11 at **5 PM**) 8 | 9 | Sep 10 10 | : **Lecture**{: .label .label-lecture } [4. Specificity and Sensitivity (Benjamini Hochberg and Neyman Pearson)](lecture/lec04) 11 | : **Discussion**{: .label .label-disc } [Discussion 2](https://drive.google.com/file/d/1yourNrwzo5obj065VBsxrjYayf4RWfgS/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1xm5j2txGwOPSgFOw6AowOdX5v8-LFXLM/view?usp=sharing)) 12 | 13 | Sep 12 14 | : **Lecture**{: .label .label-lecture } [5. Decision Theory (Loss and Risk, Frequentist and Bayesian)](lecture/lec05) 15 | : **Vitamin**{: .label .label-vit } [Vitamin 3](https://www.gradescope.com/courses/845267/assignments/4914292) (due Sep 15 at 11:59 PM) 16 | -------------------------------------------------------------------------------- /_modules/week-07.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 7: GLMs II 3 | ref: 'week-7' 4 | --- 5 | 6 | Oct 7 7 | : **Lab**{: .label .label-lab } [Lab 5.5: GLMs](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2F%2Flab%2Flab05.5%2Flab05.5.ipynb&branch=main) (due Oct 11 at **5 PM**) 8 | 9 | Oct 7 10 | : **Review Session and MiniLab**{: .label .label-disc} [Slides](https://docs.google.com/presentation/d/1g2AOe0WoPHYtR0bqCvOynOFsVy3k3novDv0rx7LPdn8/edit?usp=sharing) 11 | 12 | Oct 8 13 | : **Midterm**{: .label .label-exam } Midterm 1 14 | 15 | Oct 10 16 | : **Lecture**{: .label .label-lecture } [12. Uncertainty Quantification for GLMs](lecture/lec12) 17 | 18 | Oct 11 19 | : **Homework**{: .label .label-hw } [Homework 3](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Fhomework%2Fhw03%2Fhw3.pdf&branch=main) (due Oct 25 at **5 PM**) 20 | -------------------------------------------------------------------------------- /lecture/lec09.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 9 - Bayesian Inference with Sampling 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 9 - Bayesian Inference with Sampling 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1e6ezqnnuSTTqPYXpC1VpC1FWqn7Ha75jvro1FXMF-RU/edit?usp=sharing) (Annotated) 12 | - [Demo notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture09%2Flec09.ipynb&branch=main) 13 | - [Gibbs sampling notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture09%2Fgibbs.ipynb&branch=main) 14 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-9-bayesian-inference-with-sampling) 15 | - Textbook reading: [Chapter 2.4](https://data102.org/ds-102-book/content/chapters/02/04_inference_sampling.html) 16 | -------------------------------------------------------------------------------- /lecture_names.txt: -------------------------------------------------------------------------------- 1 | 0. No Lecture 2 | 1. Binary Decision-Making I 3 | 2. Binary Decision-Making II 4 | 3. $$p$$-Values and Multiple Hypothesis Testing 5 | 4. False Discovery Rate Control & ROC Curves 6 | 5. Frequentist vs. Bayesian Decision-Making 7 | 6. Introduction to Frequentist and Bayesian Modeling 8 | 7. Bayesian Hierarchical Models 9 | 8. Bayesian Inference with Sampling 10 | 9. Rejection Sampling and Gibbs Sampling 11 | 10. Regression and GLMs 12 | 11. Model Checking for GLMs 13 | 12. Uncertainty Quantification for GLMs 14 | 13. Nonparametric Methods and Neural Networks 15 | 14. Neural Networks and Interpretability 16 | 15. Causal Inference I: Association and Causation 17 | 16. Causal Inference II: Randomized Experiments 18 | 17. Causal Inference III: Observational Studies 19 | 18. Concentration Inequalities 20 | 19. Bandits I 21 | 20. Bandits II 22 | 21. Reinforcement Learning I 23 | 22. Reinforcement Learning II 24 | 23. Monte Carlo Tree Search 25 | 24. Privacy in Machine Learning 26 | 25. Case Studies: Robustness and Generalization 27 | 26. Course Wrap-Up 28 | -------------------------------------------------------------------------------- /lecture/lec14.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 14 - Neural Networks and Backpropagation 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 14 - Neural Networks and Backpropagation 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - Required reading - [Statistical Modeling, The Two Cultures, by Leo Breiman](http://cda.psych.uiuc.edu/statistical_learning_course/breiman_two_cultures.pdf) (pp 1-11) 12 | - [Slides](https://docs.google.com/presentation/d/18N-LwHg50CCNJ92JJhzYE3YbVK7DLIOGwm-GR14rNi0/edit?usp=sharing){:target="_blank"} [(Annotated)](https://drive.google.com/file/d/1w-JSldWAdBKUKBaJ8Pfc_j6xDUsvO5_P/view?usp=drive_link) 13 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-14-neural-networks-and-backpropagation) 14 | - [Demo and typed notes](http://data102.datahub.berkeley.edu/hub/user-redirect/git-sync?repo=https://github.com/ds-102/fa24-materials&subPath=lecture/lecture14/nns_backprop_autodiff.ipynb){:target="_blank"} 15 | - Optional supplementary video: Cynthia Rudin's [Please Stop Doing Explainable ML](https://www.youtube.com/watch?v=I0yrJz8uc5Q) 16 | -------------------------------------------------------------------------------- /lecture/lec07.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Lecture 7 - Bayesian Hierarchical Models 4 | nav_exclude: true 5 | --- 6 | 7 | # Lecture 7 - Bayesian Hierarchical Models 8 | 9 | Presented by Ramesh Sridharan 10 | 11 | - [Slides](https://docs.google.com/presentation/d/1cCOIvvHfZkOs_CA3Zuglh4azGcsygbqUGVkfbDIrFwg/edit?usp=sharing) ([Annotated](https://drive.google.com/file/d/1ccMUUvcUk15DBOnQ7V0F7gre_VT39h4_/view?usp=drive_link)) 12 | - [Demo notebook](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flecture%2Flecture07%2Flec07.ipynb&branch=main) 13 | - [Recording](https://bcourses.berkeley.edu/courses/1538676/pages/lecture-7-bayesian-hierarchical-models) 14 | - Textbook reading: 15 | - [Chapter 2.2](https://data102.org/ds-102-book/content/chapters/02/01_parameter_estimation.html) (up through and including *Educated Guess* section) 16 | - [Chapter 2.4, "Implementing models in PyMC" section only](https://data102.org/ds-102-book/content/chapters/02/04_inference_sampling.html#implementing-models-in-pymc) 17 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Data 102 at UC Berkeley 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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-------------------------------------------------------------------------------- /_modules/week-14.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 14: Privacy 3 | ref: 'week-14' 4 | --- 5 | 6 | Nov 25 7 | : **Homework**{: .label .label-hw } [Homework 6](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Fhomework%2Fhw06%2Fhw6.pdf&branch=main) (due: first half Dec 3 at **3:30 PM**, full assignment Dec 6 at **5 PM**) 8 | 9 | Nov 26 10 | : **Lab**{: .label .label-lab } [Lab 11: Reinforcement Learning](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab11%2Flab11.ipynb&branch=main) (due Dec 4 at **5 PM**) 11 | 12 | : **Lab**{: .label .label-lab } [Lab 12: Differential Privacy (Optional)](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab12%2Flab12.ipynb&branch=main) (No due date) 13 | 14 | Nov 26 15 | : **Lecture**{: .label .label-lecture } [24. Case Studies, Robustness, and Generalization](lecture/lec24) 16 | 17 | Nov 28 18 | : **Lecture**{: .label .label-lecture } Holiday 19 | -------------------------------------------------------------------------------- /_modules/week-11.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 11: Bandits 3 | ref: 'week-11' 4 | --- 5 | 6 | Nov 4 7 | : **Lab**{: .label .label-lab } [Lab 9: Unconfoundedness](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab09%2Flab09.ipynb&branch=main) (due Nov 6 at **5 PM**) 8 | 9 | 10 | Nov 5 11 | : **Lecture**{: .label .label-lecture } [19. Bandits I](lecture/lec19) 12 | : **Discussion**{: .label .label-disc } [Discussion 9](https://drive.google.com/file/d/1MJUbJbZzqLQRDeD2ewxngW-_c5f0OCib/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1QeaeAqizXc9EIUoalen1a1MD7B5bhLce/view?usp=sharing)) 13 | 14 | Nov 7 15 | : **Lecture**{: .label .label-lecture } [20. Bandits II](lecture/lec20) 16 | : **Vitamin**{: .label .label-vit } [Vitamin 10](https://www.gradescope.com/courses/845267/assignments/5273152) (due Nov 10 at 11:59 PM) 17 | 18 | Nov 8 19 | : **Homework**{: .label .label-hw } [Homework 5](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Fhomework%2Fhw05%2Fhw5.pdf&branch=main) (due Nov 22 at **5 PM**) 20 | -------------------------------------------------------------------------------- /_modules/week-02.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 2: Decisions I 3 | ref: 'week-2' 4 | --- 5 | 6 | Sep 2 7 | : **Lab**{: .label .label-lab } [Lab 1: Review and Warm-Up](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab01%2Flab01.ipynb&branch=main) (due Sep 4 at **5 PM**) 8 | 9 | Sep 3 10 | : **Lecture**{: .label .label-lecture } [2. Binary Decision-Making II](lecture/lec02) 11 | : **Discussion**{: .label .label-disc } [Discussion 1](https://drive.google.com/file/d/1_KGZs8kRbHutxizjEnNsw7osFjkPnp77/view?usp=drive_link) ([Answers](https://drive.google.com/file/d/1o5H-YJPr3ZbfqMmk4v9Aws6aCcJJWjB2/view?usp=sharing)) 12 | 13 | Sep 5 14 | : **Lecture**{: .label .label-lecture } [3. Binary Decision-Making III: Hypothesis Testing](lecture/lec03) 15 | : **Vitamin**{: .label .label-vit } [Vitamin 2](https://www.gradescope.com/courses/845267/assignments/4863612) (due Sep 8 at 11:59 PM) 16 | 17 | Sept 6 18 | : **Homework**{: .label .label-hw } [Homework 1](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Fhomework%2Fhw01%2Fhomework1.pdf&branch=main) (due Sept 20 at **5 PM**) 19 | -------------------------------------------------------------------------------- /_modules/week-09.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 9: Causal Inference I 3 | ref: 'week-9' 4 | --- 5 | Oct 21 6 | : **Lab**{: .label .label-lab } [Lab 7: Nonparametric methods](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Flab%2Flab07%2Flab07.ipynb&branch=main) (due Oct 23 at **5 PM**) 7 | 8 | Oct 22 9 | : **Lecture**{: .label .label-lecture } [15. Causal Inference I: Association and Causation](lecture/lec15) 10 | : **Discussion**{: .label .label-disc } [Discussion 7](https://drive.google.com/file/d/1AD9sOB2nHL9CKjBkQlX0j01i_TfSzYam/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1TPYRRl8xnYovqGRzSpCV7CVm--IOQKSh/view?usp=sharing)) 11 | 12 | Oct 24 13 | : **Lecture**{: .label .label-lecture } [16. Causal Inference II: Randomized Experiments](lecture/lec16) 14 | : **Vitamin**{: .label .label-vit } [Vitamin 8](https://www.gradescope.com/courses/845267/assignments/5201740) (due Oct 27 at 11:59 PM) 15 | 16 | Oct 25 17 | : **Homework**{: .label .label-hw } [Homework 4](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Fhomework%2Fhw04%2Fhw4.pdf&branch=main) (due Nov 8 at **5 PM**) 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Not sure what a .gitignore is? 2 | # See: https://git-scm.com/docs/gitignore 3 | 4 | # The first files are directly copied from Jekyll's first-party docs on `.gitignore` files: 5 | # https://jekyllrb.com/tutorials/using-jekyll-with-bundler/#commit-to-source-control 6 | 7 | # Ignore the default location of the built site, and caches and metadata generated by Jekyll 8 | _site/ 9 | .sass-cache/ 10 | .jekyll-cache/ 11 | .jekyll-metadata 12 | 13 | # Ignore folders generated by Bundler 14 | .bundle/ 15 | vendor/ 16 | 17 | # These next files are used by Just the Docs developers. They are not necessary for end users of the theme, only developers. 18 | 19 | # We use Stylelint and Prettier, JavaScript tools, to lint and format our own code. 20 | # We use Node.js as our runtime, so we ignore node_modules 21 | node_modules 22 | 23 | # .DS_Store is a macOS-only metadata file about directories. Convention is to not commit them. 24 | # See: https://en.wikipedia.org/wiki/.DS_Store 25 | .DS_Store 26 | 27 | # .ipynb_checkpoints are directories created when a .ipynb file is created. They are local files, so they shouldn't be committed. 28 | .ipynb_checkpoints 29 | 30 | # These are legacy globs that typically target Ruby theme developers. We may change these at a later date. 31 | *.gem 32 | .bundle 33 | .ruby-version 34 | -------------------------------------------------------------------------------- /_modules/week-04.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Week 4: Bayesian Modeling I 3 | ref: 'week-4' 4 | --- 5 | Sep 16 6 | : **Lab**{: .label .label-lab } [Lab 3: Loss and Risk](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2F%2Flab%2Flab03%2Flab03.ipynb&branch=main) (due Sep 18 at **5 PM**) 7 | 8 | Sep 17 9 | : **Lecture**{: .label .label-lecture } [6. Parameter Estimation and Inference: Introduction to Frequentist and Bayesian Modeling](lecture/lec06) 10 | : **Discussion**{: .label .label-disc } [Discussion 3](https://drive.google.com/file/d/1KdkLWwpdIMxgQotYYDK3g7EXDtKAvPmo/view?usp=sharing) ([Answers](https://drive.google.com/file/d/1NywY2oCPaEFw3BGzUMckXRVim9KTZLVl/view?usp=sharing)) 11 | 12 | Sep 19 13 | : **Lecture**{: .label .label-lecture } [7. Bayesian Hierarchical Models](lecture/lec07) 14 | : **Vitamin**{: .label .label-vit } [Vitamin 4](https://www.gradescope.com/courses/845267/assignments/4914348) (due Sep 22 at 11:59 PM) 15 | 16 | Sept 20 17 | : **Homework**{: .label .label-hw } [Homework 2](https://data102.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fds-102%2Ffa24-materials&urlpath=lab%2Ftree%2Ffa24-materials%2Fhomework%2Fhw02%2Fhw2.pdf&branch=main) (due Oct 4 at **5 PM**) 18 | -------------------------------------------------------------------------------- /calendar.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Calendar 4 | nav_order: 4 5 | description: Calendars with times for all course events. 6 | --- 7 | 8 | # Calendar 9 | 10 | All course events are listed in the following calendar. Click on the events to check the locations. 11 | 12 | 13 | -------------------------------------------------------------------------------- /staff.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Staff 4 | nav_order: 7 5 | description: A listing of all the course staff members. 6 | --- 7 | 8 | # Staff 9 | 10 | Append `@berkeley.edu` to all email addresses. **For personal circumstances or sensitive matters,** please use the staff email address **[data102@berkeley.edu](mailto:data102@berkeley.edu)**, which is monitored only by the instructors and a few lead TAs. 11 | 12 | **Note:** Consult the [calendar]({{site.baseurl}}/calendar) for the most up-to-date office hours. 13 | 14 | {% assign professors = site.staffers | where: 'role', 'Professor' | reverse %} 15 | {% assign num_professors = professors | size %} 16 | {% if num_professors != 0 %} 17 | ## Professors 18 | 19 |
20 | {% for staffer in professors %} 21 | {{ staffer }} 22 | {% endfor %} 23 |
24 | {% endif %} 25 | 26 | {% assign instructors = site.staffers | where: 'role', 'Instructor' | reverse %} 27 | {% assign num_instructors = instructors | size %} 28 | {% if num_instructors != 0 %} 29 | ## Instructors 30 | 31 | {% for staffer in instructors %} 32 | {{ staffer }} 33 | {% endfor %} 34 | {% endif %} 35 | 36 | {% assign staff = site.staffers | where: 'team', 'Staff' | reverse %} 37 | {% assign num_staff = staff | size %} 38 | {% if num_staff != 0 %} 39 | ## Course Staff 40 | 41 |
42 | {% for staffer in staff %} 43 | {{ staffer }} 44 | {% endfor %} 45 |
46 | {% endif %} 47 | -------------------------------------------------------------------------------- /_layouts/schedule.html: -------------------------------------------------------------------------------- 1 | {% assign start_time = page.timeline | first %} 2 | {% capture offset %}{% include minutes.liquid time=start_time %}{% endcapture %} 3 |
4 | 9 | 34 |
35 | -------------------------------------------------------------------------------- /initial_setup.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime, timedelta 2 | 3 | content = """--- 4 | title: Week {week}: {week_topic} 5 | ref: 'week-{week}' 6 | --- 7 | 8 | {tues_date} 9 | : **Lecture**{{: .label .label-lecture }} {tues_topic} 10 | 11 | {thurs_date} 12 | : **Lecture**{{: .label .label-lecture }} {thurs_topic} 13 | """ 14 | 15 | 16 | def do_stuff(): 17 | week_topics = [ 18 | 'Introductions', 'Decisions I', 'Decisions II', 19 | 'Bayesian Modeling I', 'Bayesian Modeling II', 20 | 'GLMs I', 'GLMs II', 'Nonparametric Methods', 21 | 'Causal Inference I', 'Causal Inference II', 22 | 'Bandits', 'Reinforcement Learning I', 23 | 'Reinforcement Learning II', 'Privacy', 'Wrapup' 24 | ] 25 | with open('lecture_names.txt') as f: 26 | lecture_topics = [line.strip() for line in f.readlines()] 27 | current = datetime(2024, 8, 27) 28 | midterms = [datetime(2024, 10, 8), datetime(2024, 11, 14)] 29 | holidays = [datetime(2024, 11, 28)] 30 | midterm_number = 1 31 | for week, week_topic in enumerate(week_topics, 1): 32 | 33 | tues = current 34 | thurs = current + timedelta(2) 35 | 36 | week_params = { 37 | 'week': week, 38 | 'week_topic': week_topic, 39 | 'tues_date': tues.strftime('%b %-d'), 40 | 'thurs_date': thurs.strftime('%b %-d'), 41 | } 42 | for lec_date, day in [ 43 | (tues, 'tues'), (thurs, 'thurs') 44 | ]: 45 | if lec_date in holidays: 46 | topic = 'Holiday' 47 | elif lec_date in midterms: 48 | topic = f'Midterm {midterm_number}' 49 | midterm_number += 1 50 | else: 51 | topic = lecture_topics.pop(0) 52 | week_params[f'{day}_topic'] = topic 53 | write_week(week, week_params) 54 | current += timedelta(days=7) 55 | 56 | 57 | def write_week(week, week_params): 58 | with open(f'_modules/week-{week:02d}.md', 'w') as f: 59 | f.write(content.format(**week_params)) 60 | 61 | 62 | if __name__ == '__main__': 63 | do_stuff() 64 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data 102 Fall 2024 Website 2 | 3 | This [video](https://www.youtube.com/watch?v=azPPK5aOcV0) walks you through how to make changes to the website (just replace all instances of fa19 with fa23 and DS100 with Data 102). 4 | 5 | To add Jupyter notebook links to the webpage, use nbgitpuller: https://jupyterhub.github.io/nbgitpuller/link 6 | 7 | The following text is taken from the standard GitHub Pages README. 8 | 9 | 10 | You can use the editor on GitHub to maintain and preview the content for your website in Markdown files. 11 | 12 | Whenever you commit to this repository, GitHub Pages will run Jekyll to rebuild the pages in your site, from the content in your Markdown files. 13 | 14 | ### Markdown 15 | Markdown is a lightweight and easy-to-use syntax for styling your writing. It includes conventions for 16 | 17 | ```markdown 18 | Syntax highlighted code block 19 | 20 | # Header 1 21 | ## Header 2 22 | ### Header 3 23 | 24 | - Bulleted 25 | - List 26 | 27 | 1. Numbered 28 | 2. List 29 | 30 | **Bold** and _Italic_ and `Code` text 31 | 32 | [Link](url) and ![Image](src) 33 | For more details see GitHub Flavored Markdown. 34 | ``` 35 | 36 | ### Liquid 37 | Here's some good references for using `liquid`. 38 | - https://shopify.github.io/liquid/ 39 | - https://shopify.dev/docs/api/liquid 40 | 41 | ### Jekyll Themes 42 | Your Pages site will use the layout and styles from the Jekyll theme you have selected in your repository settings. The name of this theme is saved in the Jekyll _config.yml configuration file. 43 | 44 | ### Support or Contact 45 | Having trouble with Pages? Check out our documentation or contact support and we’ll help you sort it out. 46 | 47 | ### Running Locally 48 | This website is written using Jekyll Bootstrap with some modifications to improve support for github pages. 49 | 50 | Install `rvm`: https://rvm.io/ or `rbenv`: https://github.com/rbenv/rbenv. Personally, `rbenv` might work better for me. 51 | 52 | Install Ruby 2.2.0: 53 | 54 | ``rvm install 2.2.0`` 55 | 56 | Clone this repo: 57 | 58 | ``git clone https://github.com/ds-102/fa23.git`` 59 | 60 | In the repo directory, run: 61 | 62 | ``gem install bundler`` 63 | ``bundle install`` 64 | 65 | Finally, serve the project locally with: 66 | 67 | ``jekyll serve`` 68 | 69 | This will start the local Jekyll server at http://localhost:4000. 70 | -------------------------------------------------------------------------------- /_sass/custom/schedule.scss: -------------------------------------------------------------------------------- 1 | .schedule { 2 | @include abstract-card(); 3 | overflow-x: scroll; 4 | position: relative; 5 | 6 | li::before { 7 | display: none; 8 | } 9 | 10 | ul.schedule-timeline, 11 | ul.schedule-group, 12 | ul.schedule-events { 13 | margin-top: 0; 14 | padding-left: 0; 15 | } 16 | 17 | ul.schedule-timeline { 18 | margin: 40px auto 0; 19 | position: absolute; 20 | width: 100%; 21 | } 22 | 23 | .schedule-time { 24 | @extend .fs-2; 25 | color: $grey-dk-000; 26 | height: 40px; 27 | margin: 0; 28 | padding: $sp-2; 29 | position: relative; 30 | 31 | &::after { 32 | background-color: $border-color; 33 | content: ''; 34 | height: 1px; 35 | left: 0; 36 | position: absolute; 37 | top: 0; 38 | width: 100%; 39 | } 40 | } 41 | 42 | .schedule-group { 43 | display: flex; 44 | margin-bottom: 0; 45 | position: relative; 46 | } 47 | 48 | .schedule-day { 49 | border-left: $border $border-color; 50 | flex: 1 0 0; 51 | margin: 0; 52 | min-width: 120px; 53 | 54 | &:first-of-type { 55 | border-left: 0; 56 | } 57 | } 58 | 59 | h2.schedule-header { 60 | align-items: center; 61 | display: flex; 62 | font-size: 18px !important; 63 | height: 40px; 64 | justify-content: center; 65 | margin: 0; 66 | } 67 | 68 | .schedule-events { 69 | display: flex; 70 | padding: 0; 71 | position: relative; 72 | } 73 | 74 | .schedule-event { 75 | background-color: $grey-dk-000; 76 | border-radius: $border-radius; 77 | box-shadow: 0 10px 20px rgba(0, 0, 0, .1), inset 0 -3px 0 rgba(0, 0, 0, .2); 78 | color: $white; 79 | float: left; 80 | height: 100%; 81 | margin: 0; 82 | padding: $sp-1 $sp-2; 83 | position: absolute; 84 | width: 100%; 85 | 86 | .name { 87 | @extend .fs-3, .fw-700; 88 | } 89 | 90 | .time, 91 | .location { 92 | @extend .fs-2; 93 | } 94 | 95 | &.lecture { 96 | background-color: $grey-dk-000; 97 | } 98 | 99 | &.section { 100 | background-color: $purple-000; 101 | } 102 | 103 | &.office-hours { 104 | background-color: $blue-000; 105 | } 106 | } 107 | } 108 | -------------------------------------------------------------------------------- /.github/workflows/jekyll.yml: -------------------------------------------------------------------------------- 1 | # This workflow uses actions that are not certified by GitHub. 2 | # They are provided by a third-party and are governed by 3 | # separate terms of service, privacy policy, and support 4 | # documentation. 5 | 6 | # Sample workflow for building and deploying a Jekyll site to GitHub Pages 7 | name: Deploy Jekyll site to Pages 8 | 9 | on: 10 | # Runs on pushes targeting the default branch 11 | push: 12 | branches: ["main"] 13 | 14 | # Allows you to run this workflow manually from the Actions tab 15 | workflow_dispatch: 16 | 17 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages 18 | permissions: 19 | contents: read 20 | pages: write 21 | id-token: write 22 | 23 | # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. 24 | # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. 25 | concurrency: 26 | group: "pages" 27 | cancel-in-progress: false 28 | 29 | jobs: 30 | # Build job 31 | build: 32 | runs-on: ubuntu-latest 33 | steps: 34 | - name: Checkout 35 | uses: actions/checkout@v4 36 | - name: Setup Ruby 37 | uses: ruby/setup-ruby@8575951200e472d5f2d95c625da0c7bec8217c42 # v1.161.0 38 | with: 39 | ruby-version: '3.1' # Not needed with a .ruby-version file 40 | bundler-cache: true # runs 'bundle install' and caches installed gems automatically 41 | cache-version: 0 # Increment this number if you need to re-download cached gems 42 | - name: Setup Pages 43 | id: pages 44 | uses: actions/configure-pages@v4 45 | - name: Build with Jekyll 46 | # Outputs to the './_site' directory by default 47 | run: bundle exec jekyll build --baseurl "${{ steps.pages.outputs.base_path }}" 48 | env: 49 | JEKYLL_ENV: production 50 | - name: Upload artifact 51 | # Automatically uploads an artifact from the './_site' directory by default 52 | uses: actions/upload-pages-artifact@v3 53 | 54 | # Deployment job 55 | deploy: 56 | environment: 57 | name: github-pages 58 | url: ${{ steps.deployment.outputs.page_url }} 59 | runs-on: ubuntu-latest 60 | needs: build 61 | steps: 62 | - name: Deploy to GitHub Pages 63 | id: deployment 64 | uses: actions/deploy-pages@v4 65 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | # Welcome to Jekyll! 2 | # 3 | # This config file is meant for settings that affect your whole site, values 4 | # which you are expected to set up once and rarely edit after that. If you find 5 | # yourself editing these this file very often, consider using Jekyll's data files 6 | # feature for the data you need to update frequently. 7 | # 8 | # For technical reasons, this file is *NOT* reloaded automatically when you use 9 | # 'jekyll serve'. If you change this file, please restart the server process. 10 | 11 | # Site settings 12 | # These are used to personalize your new site. If you look in the HTML files, 13 | # you will see them accessed via {{ site.title }}, {{ site.github_repo }}, and so on. 14 | # You can create any custom variable you would like, and they will be accessible 15 | # in the templates via {{ site.myvariable }}. 16 | title: Data 102 17 | tagline: Data, Inference, and Decisions 18 | description: The course website for UC Berkeley's Data 102 course 19 | author: Ramesh Sridharan 20 | baseurl: '/fa24' # the subpath of your site, e.g. /blog 21 | url: 'https://data102.org' # the base hostname & protocol for your site, e.g. http://example.com 22 | logo: 'assets/brand/favicon.png' 23 | current_week: 1 24 | 25 | # Theme settings 26 | remote_theme: just-the-docs/just-the-docs@v0.5.2 27 | color_scheme: wider 28 | search_enabled: true 29 | permalink: pretty 30 | heading_anchors: false 31 | 32 | aux_links: 33 | Ed: 34 | - 'https://edstem.org/us/courses/52891' 35 | Gradescope: 36 | - 'https://www.gradescope.com/courses/711377' 37 | # Extenuating Circumstances: 38 | # - 'https://docs.google.com/forms/d/e/1FAIpQLSerF4KsagLRLNPIfRnHPT1B_RuGRRAAl2CBdLz-tvMY2mbjYQ/viewform?usp=sf_link' 39 | 40 | # Collections for website data 41 | collections: 42 | - staffers 43 | - announcements 44 | - modules 45 | # Default layouts for each collection type 46 | defaults: 47 | - scope: 48 | path: '' 49 | type: staffers 50 | values: 51 | layout: staffer 52 | height: 300 53 | subpath: '/assets/staff/' 54 | width: 300 55 | - scope: 56 | path: '' 57 | type: announcements 58 | values: 59 | layout: announcement 60 | - scope: 61 | path: '' 62 | type: modules 63 | values: 64 | layout: module 65 | 66 | callouts: 67 | highlight: 68 | color: yellow 69 | warning: 70 | title: Warning 71 | color: red 72 | 73 | compress_html: 74 | clippings: all 75 | comments: all 76 | endings: all 77 | startings: [] 78 | blanklines: false 79 | profile: false 80 | 81 | math: mathjax 82 | -------------------------------------------------------------------------------- /_sass/custom/module.scss: -------------------------------------------------------------------------------- 1 | .main-content .module, 2 | .module { 3 | @extend %card; 4 | 5 | h1, 6 | h2, 7 | h3, 8 | h4, 9 | h5, 10 | h6 { 11 | &:first-child { 12 | margin-top: $sp-4; 13 | } 14 | } 15 | 16 | >dl { 17 | border-bottom: $border $border-color; 18 | border-top: $border $border-color; 19 | display: grid; 20 | grid-template-columns: max-content 1fr; 21 | margin: $sp-2 (-$sp-4); 22 | 23 | &:first-child { 24 | margin-top: 0; 25 | } 26 | 27 | &:last-child { 28 | margin-bottom: 0; 29 | } 30 | 31 | @include mq(lg) { 32 | grid-template-columns: 1fr 7fr; 33 | } 34 | 35 | %module-item { 36 | margin: 0; 37 | padding: $sp-2; 38 | 39 | @include mq(sm) { 40 | padding: $sp-2 $sp-4; 41 | } 42 | } 43 | 44 | >dt { 45 | @extend %module-item; 46 | border-top: $border $border-color; 47 | font-weight: normal; 48 | text-align: right; 49 | 50 | +dd { 51 | border-top: $border $border-color; 52 | } 53 | 54 | &:first-child { 55 | border-top: none; 56 | 57 | +dd { 58 | border-top: none; 59 | } 60 | } 61 | 62 | &::after { 63 | content: ":"; 64 | } 65 | } 66 | 67 | >dd { 68 | @extend %module-item; 69 | 70 | +dd { 71 | padding-top: 0; 72 | } 73 | 74 | ol, ul, dl { 75 | margin: 0; 76 | } 77 | 78 | dl { 79 | display: flex; 80 | flex-direction: column; 81 | 82 | @include mq(sm) { 83 | flex-direction: row; 84 | } 85 | 86 | dt { 87 | flex: 0 0 80%; 88 | margin: 0; 89 | } 90 | 91 | dd { 92 | margin: 0; 93 | } 94 | } 95 | } 96 | 97 | .label-lecture { background-color: #5C5962; border-radius: 9px; } 98 | 99 | .label-lab { background-color: #0461b8; border-radius: 9px; } 100 | 101 | .label-vit { background-color: #c1443c; border-radius: 9px; } 102 | 103 | .label-hw { background-color: #5E41D0; border-radius: 9px; } 104 | 105 | .label-exam { background-color: #E62100; border-radius: 9px; } 106 | 107 | .label-crit { background-color: #FF8144; border-radius: 9px; } 108 | 109 | .label-disc { background-color: #498226; border-radius: 9px; } 110 | 111 | .label-survey { background-color: #aaaaaa; border-radius: 9px; } 112 | 113 | .label-proj { background-color: #FF8144; border-radius: 9px; } 114 | 115 | } 116 | } -------------------------------------------------------------------------------- /_sass/custom/custom.scss: -------------------------------------------------------------------------------- 1 | // Just the Class dependencies 2 | @import 'card'; 3 | 4 | // Just the Class styles 5 | 6 | @import 'variables'; 7 | @import 'announcement'; 8 | @import 'module'; 9 | @import 'schedule'; 10 | @import 'staffer'; 11 | 12 | // Overrides 13 | @media (min-width: 66.5rem) { 14 | .side-bar { width: 248px !important} 15 | .main { margin-left: 264px !important;} 16 | } 17 | 18 | @media (min-width: 50rem) { 19 | .main { max-width: 100% !important;} 20 | 21 | .site-header { 22 | max-height: 150px !important; 23 | height: 100% !important; 24 | } 25 | 26 | .search-input { 27 | font-size: 0.9em !important; 28 | } 29 | 30 | } 31 | 32 | @media (max-width: 50rem) { 33 | .site-logo { 34 | margin-left: 60px !important; 35 | height: 60px !important; 36 | } 37 | } 38 | 39 | .aux-nav { 40 | font-size: 0.9em !important; 41 | } 42 | 43 | .site-logo { 44 | background-position: center !important; 45 | } 46 | 47 | .flex { 48 | display: flex; 49 | flex-wrap: wrap; 50 | } 51 | 52 | .site-footer { 53 | color: #000000 54 | } 55 | 56 | code { 57 | font-size: 14px; 58 | padding: 0.2em 0.4em; 59 | border: none; 60 | } 61 | 62 | iframe { 63 | max-width: 100%; 64 | } 65 | 66 | details { 67 | @extend .mb-4; 68 | } 69 | 70 | summary { 71 | @extend .btn, .btn-outline; 72 | 73 | width: 100%; 74 | } 75 | 76 | .main-content-wrap { 77 | max-width: $content-width; 78 | margin: auto; 79 | } 80 | 81 | a { 82 | color: $blue-100; 83 | } 84 | 85 | .logo { 86 | margin: 0 auto; 87 | max-width: 100%; 88 | width: 100%; 89 | } 90 | 91 | .main-content { 92 | dl { 93 | display: block; 94 | grid-template-columns: none; 95 | } 96 | 97 | dt { 98 | font-weight: normal; 99 | text-align: start; 100 | 101 | &::after { 102 | content: normal; 103 | } 104 | } 105 | 106 | dd { 107 | font-weight: normal; 108 | 109 | + dt { 110 | margin-top: 1em; 111 | } 112 | } 113 | } 114 | 115 | .pronouns { 116 | font-weight: bold !important; 117 | display: inline-block; 118 | padding-top: 0.16em; 119 | padding-bottom: 0.16em; 120 | padding-left: 0.56em; 121 | padding-right: 0.56em; 122 | 123 | font-size: 0.8em; 124 | 125 | text-transform: uppercase; 126 | vertical-align: middle; 127 | border-radius: 12px; 128 | 129 | background-color: #ecebed; 130 | } 131 | 132 | .team { 133 | font-weight: bold !important; 134 | display: inline-block; 135 | padding-top: 0.16em; 136 | padding-bottom: 0.16em; 137 | padding-left: 0.56em; 138 | padding-right: 0.56em; 139 | 140 | font-size: 0.8em; 141 | 142 | vertical-align: middle; 143 | border-radius: 6px; 144 | 145 | } 146 | 147 | .label-Head { 148 | background-color: #3D348B; 149 | color: white; 150 | } 151 | 152 | .btn-datahub { 153 | @include btn-color($white, #FF8C00); 154 | } 155 | 156 | .btn-ed { 157 | @include btn-color($white, #9400D3); 158 | } 159 | 160 | .btn-gradescope { 161 | @include btn-color($white, #228B22); 162 | } -------------------------------------------------------------------------------- /Gemfile.lock: -------------------------------------------------------------------------------- 1 | GEM 2 | remote: https://rubygems.org/ 3 | specs: 4 | activesupport (7.0.5) 5 | concurrent-ruby (~> 1.0, >= 1.0.2) 6 | i18n (>= 1.6, < 2) 7 | minitest (>= 5.1) 8 | tzinfo (~> 2.0) 9 | addressable (2.8.4) 10 | public_suffix (>= 2.0.2, < 6.0) 11 | coffee-script (2.4.1) 12 | coffee-script-source 13 | execjs 14 | coffee-script-source (1.11.1) 15 | colorator (1.1.0) 16 | commonmarker (0.23.9) 17 | concurrent-ruby (1.2.2) 18 | dnsruby (1.70.0) 19 | simpleidn (~> 0.2.1) 20 | em-websocket (0.5.3) 21 | eventmachine (>= 0.12.9) 22 | 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263 | webrick (~> 1.8) 264 | 265 | BUNDLED WITH 266 | 2.4.13 267 | -------------------------------------------------------------------------------- /resources.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Resources 4 | nav_order: 6 5 | description: Past exams and other relevant material to aid you in the course. 6 | 7 | --- 8 | 9 | # Resources 10 | {:.no_toc} 11 | 12 | Here is a collection of resources that will help you learn more about various concepts and skills covered in the class. Learning by reading is a key part of being a well rounded data scientist. We will not assign mandatory reading but instead encourage you to look at these _and other_ materials. If you find something helpful, post it on EdStem, and consider contributing it to the course website. 13 | 14 | Jump to: 15 | * TOC 16 | {:toc} 17 | 18 | ## Exam Resources 19 | 20 | | Semester | Midterm (1) | Midterm 2 | Final | 21 | | -- | -- | -- | -- | 22 | | Fall 2024 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/fa24_mt1.pdf) ([Solutions]({{ site.baseurl }}/assets/exams/midterm_1/fa24_mt1_sol.pdf)) | [Exam]({{ site.baseurl }}/assets/exams/midterm_2/fa24_mt2.pdf) ([Solutions]({{ site.baseurl }}/assets/exams/midterm_2/fa24_mt2_sol.pdf))| | 23 | | Spring 2024 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/sp24_mt1.pdf) ([Solutions]({{ site.baseurl }}/assets/exams/midterm_1/sp24_mt1_sol.pdf)) |[Exam]({{ site.baseurl }}/assets/exams/midterm_2/sp24_mt2.pdf) ([Solutions](assets/exams/midterm_2/sp24_mt2_sol.pdf)) | | 24 | | Fall 2023 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/fa23_mt1.pdf) ([Solutions]({{ site.baseurl }}/assets/exams/midterm_1/fa23_mt1_sol.pdf)) |[Exam]({{ site.baseurl }}/assets/exams/midterm_2/fa23_mt2.pdf) ([Solutions](https://drive.google.com/file/d/1A4QUNqItI31id6Zl7nUfC-FZybTzXbm1/view?usp=sharing)) | | 25 | | Spring 2023 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/sp23_mt1.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/sp23_mt1_sol.pdf) | [Exam]({{ site.baseurl }}/assets/exams/midterm_2/sp23_mt2.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_2/sp23_mt2_sol.pdf) | | 26 | | Fall 2022 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/fa22_mt1.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/fa22_mt1_sol.pdf) | [Exam]({{ site.baseurl }}/assets/exams/midterm_2/fa22_mt2.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_2/fa22_mt2_sol.pdf) | | 27 | | Spring 2022 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/sp22_mt1.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/sp22_mt1_sol.pdf) | [Exam]({{ site.baseurl }}/assets/exams/midterm_2/sp22_mt2.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_2/sp22_mt2_sol.pdf) | | 28 | | Fall 2021 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/fa21_mt1.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/fa21_mt1_sol.pdf) | [Exam]({{ site.baseurl }}/assets/exams/midterm_2/fa21_mt2.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_2/fa21_mt2_sol.pdf) | | 29 | | Spring 2021 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/sp21_mt1.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/sp21_mt1_sol.pdf) | [Exam]({{ site.baseurl }}/assets/exams/midterm_2/sp21_mt2.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_2/sp21_mt2_sol.pdf) | | 30 | | Fall 2020 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/fa20_mt.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/fa20_mt_sol.pdf) | | [Exam]({{ site.baseurl }}/assets/exams/final/fa20_final.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/final/fa20_final_sol.pdf) | 31 | | Spring 2020 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/sp20_mt.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/sp20_mt_sol.pdf) | | | 32 | | Fall 2019 | [Exam]({{ site.baseurl }}/assets/exams/midterm_1/fa19_mt.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/midterm_1/fa19_mt_sol.pdf) | | [Exam]({{ site.baseurl }}/assets/exams/final/fa19_final.pdf) [(Solutions)]({{ site.baseurl }}/assets/exams/final/fa19_final_sol.pdf) | 33 | 34 | 35 | Here is a collection of resources that may help you learn more about various concepts and skills covered in the class. Learning by reading is a key part of being a well-rounded data scientist. We will not assign mandatory reading but instead encourage you to look at these materials. 36 | 37 | ## Web References 38 | 39 | In this class we will be using several key libraries. Here are their documentation pages: 40 | 41 | 44 | 45 | * **Python:** 46 | * [Python Tutorial](https://docs.python.org/3.5/tutorial/): Teach yourself python. This is a pretty comprehensive tutorial. 47 | * [Python + Numpy Tutorial](http://cs231n.github.io/python-numpy-tutorial/) this tutorial provides a great overview of a lot of the functionality we will be using in DS102. 48 | * [Python 101](http://nbviewer.jupyter.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Python_101.ipynb): A notebook demonstrating a lot of python functionality with some (minimal explanation). 49 | 50 | 51 | * **Plotting:** 52 | * [matplotlib.pyplot tutorial](http://matplotlib.org/users/pyplot_tutorial.html#pyplot-tutorial): This short tutorial provides an overview of the basic plotting utilities we will be using. 53 | * [seaborn](http://seaborn.pydata.org/tutorial.html): The Seaborn library has some nice additional visualization functions that we may use occasionally. 54 | 55 | * **Pandas:** 56 | * [The Pandas Cookbook](http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/tree/master/cookbook/): This provides a nice overview of some of the basic Pandas functions. However, it is slightly out of date. 57 | * [Learn Pandas](https://bitbucket.org/hrojas/learn-pandas) A set of lessons providing an overview of the Pandas library. 58 | * [Python for Data Science](http://wavedatalab.github.io/datawithpython/index.html) Another set of notebook demonstrating Pandas functionality. 59 | * [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) ([Available as eBook for Berkeley students](http://proquest.safaribooksonline.com/9781449323592)). This book provides a good reference for the Pandas library. 60 | 61 | 62 | ## Textbooks from Previous Data Science Courses 63 | 64 | Data 102 builds on material taught in previous data science courses. You may find the textbooks from those courses helpful: 65 | 66 | * **[Data 8](https://inferentialthinking.com/chapters/intro.html)** 67 | * **[Data 100](https://ds100.org/course-notes-su23/)** 68 | * **[Data 140](http://prob140.org/textbook/content/README.html)**: even if you took one of the other probability prerequisite courses, this book can be a helpful reference. 69 | 70 | ## Reading Resources 71 | 72 | * **[Data 102 Textbook](https://data102.org/ds-102-book/)** Because data science is a relatively new and rapidly evolving discipline there is no single *ideal* textbook for this subject. The instructors are in the process of developing this online textbook for the course, they will be updating the textbook as semester progresses. 73 | You can also find useful reading among the following collection of books, all of which are free 74 | 75 | * **[Patterns, Predictions, and Actions](https://mlstory.org/)** This book is a great introduction to many of the topics we cover in this course, as well as several other important topics in advanced machine learning and data science. The following chapters are particularly relevant to this class: 76 | * Chapter 2 covers decision theory. 77 | * Chapter 8 covers datasets: even though we won't be talking about this much in Data 102, this is an extremely important topic to know about for doing real-world data science. 78 | * Chapters 9 and 10 cover causal inference. 79 | * Chapter 12 covers reinforcement learning. 80 | 81 | * **[Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)** This popular online graduate course is an excellent introduction to thinking about statistics through a Bayesian and causal lens. While it goes into much more detail than Data 102 does, lectures 2, 5, and 8 are all closely related to what we cover. 82 | 83 | * **[All of Statistics](https://www.stat.cmu.edu/~larry/all-of-statistics/)** This book is a great, broad introduction to mathematical statistics. It begins with probability concepts (e.g. Bayes’ theorem), works through many statistical inference topics (e.g. hypothesis testing, decision theory, and bootstrap, and also includes statistical modeling (e.g. regression and causal inference)). The textbook as a whole covers many more ideas from statistics than will be used in or needed for this course, but students may still find it useful to reference specific topics within it to supplement ideas covered in lecture or review ideas from previous courses. For example: 84 | * Chapters 1-3 review some background ideas about probability and random variables 85 | * Chapter 12 discusses the statistical decision theory framework 86 | * Section 9.3 reviews maximum likelihood estimation, while the first few sections of chapter 11 review the core idea behind Bayesian inference 87 | * Sections 10.2, 10.6, and 10.7 cover p-values, the likelihood ratio test, and multiple testing ideas 88 | * Chapter 13 covers linear and logistic regression 89 | * Chapters 7-8 review empirical distributions and bootstrap 90 | * Chapter 16 covers causal inference 91 | 92 | * **[Computer-Age Statistical Inference](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)** This book takes a fairly modern view of statistics, often examining the influence of computation on the field. It is useful to keep in mind that the book was written with masters’ students in mind. As such, this textbook covers many topics beyond the scope of this course, but nevertheless provides useful, high-level discussion of some course topics for those students looking for additional information. For example: 93 | * Chapters 2 and 3 do an excellent job of comparing and contrasting frequentist and Bayesian inference, with illustrative examples 94 | * Chapter 4 discusses maximum likelihood estimation 95 | * Chapter 15 provides additional details about multiple hypothesis testing and false discovery rate control 96 | * There is also one section each on logistic regression, the EM algorithm, the bootstrap, conjugate priors, and Gibbs sampling 97 | 98 | * **[Causal Inference: The Mixtape](https://mixtape.scunning.com/)** This book is a great introduction and reference for all things causal inference. 99 | 100 | * **[Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)** (Free online PDF) This book is a great reference for the machine learning and some of the statistics material in the class 101 | 102 | * **[Data Science from Scratch](http://shop.oreilly.com/product/0636920033400.do)** ([Available as eBook for Berkeley students](http://proquest.safaribooksonline.com/9781491901410)) This more applied book covers many of the topics in this class using Python but doesn't go into sufficient depth for some of the more mathematical material. 103 | 104 | * **[Doing Data Science](http://shop.oreilly.com/product/0636920028529.do)** 105 | ([Available as eBook for Berkeley students](http://proquest.safaribooksonline.com/9781449363871)) This books provides a unique case-study view of data science but uses R and not Python. 106 | 107 | * **[Matrix Cookbook](https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf)** This “cookbook” is a handy collection of facts about linear algebra and matrices. -------------------------------------------------------------------------------- /syllabus.md: -------------------------------------------------------------------------------- 1 | --- 2 | layout: page 3 | title: Syllabus 4 | nav_order: 2 5 | description: The course syllabus 6 | --- 7 | 8 | # Syllabus 9 | {:.no_toc} 10 | 11 | ## Table of Contents 12 | {: .no_toc .text-delta } 13 | 14 | - TOC 15 | {:toc} 16 | 17 | 18 | ## About the Course 19 | 20 | This course develops the probabilistic foundations of inference in data science. It builds a comprehensive view of the decision-making and modeling life cycle in data science, including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, robustness, Thompson sampling, optimal control, Q-learning, differential privacy, fairness in classification, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods. 21 | 22 | This class is listed as [Data 102](https://classes.berkeley.edu/content/2024-spring-data-c102-001-lec-001). 23 | 24 | 25 | ## Prerequisites 26 | 27 | We currently require the following (or equivalent) prerequisites: 28 | 29 | 1. **Principles and Techniques of Data Science**: [Data 100](http://ds100.org/) covers important computational and statistical skills that will be necessary for Data 102. 30 | 31 | 1. **Probability**: [Data 140](http://prob140.org/), [EECS 126](https://inst.eecs.berkeley.edu/~ee126), [STAT 134](https://www.stat134.org/), [IEOR 172](https://guide.berkeley.edu/courses/ind_eng/), or [Math 106](https://guide.berkeley.edu/courses/math/). Data 140 and EECS 126 are preferred. These courses cover the probabilistic tools that will form the underpinning for the concepts covered in Data 102. 32 | 33 | 1. **Math**: Math 54, Math 56, Math 110, both EE 16A and EE 16B, STAT 89a, or Physics 89. We will need some basic concepts like linear operators, eigenvectors, derivatives, and integrals to enable statistical inference and derive new prediction algorithms. 34 | 35 | Please consult the [Resources]({{ site.baseurl }}/resources) page for additional resources for reviewing prerequisite material. 36 | 37 | 38 | ## Course Components 39 | 40 | ### Lectures 41 | {:.no_toc} 42 | 43 | Lectures will be held in-person **Tuesdays and Thursdays** from **3:30 - 5 PM in 105 Stanley**. Recordings will be made available on the course website within 24 hours. 44 | 45 | ### Discussion 46 | {:.no_toc} 47 | Discussion section will be held on **Wednesdays**, led by your GSIs. These sections will cover important problem-solving skills that bridge the concepts in lectures with the skills you’ll need to apply the ideas on the homework and beyond. 48 | 49 | ### Lab 50 | {:.no_toc} 51 | Labs will be held on **Mondays** by GSIs. You will be working on lab assignments with your GSI in these sections. You can complete the assignments on your own time, but you are highly encouraged to attend lab sessions to work with your classmates and get help from the staff. Help will be limited on Ed and office hours because of this. 52 | 53 | When completing and submitting the lab, you may work individually or in pairs. You may pair up with any enrolled or waitlisted student for each lab: you do not have to be in the same lab section or work with the same person every week. 54 | 55 | ### Homeworks 56 | {:.no_toc} 57 | Homework assignments are released every other week on Fridays and due two Fridays after. These assignments are designed to help students develop an in-depth understanding of both the theoretical and practical aspects of ideas presented in lectures. They contain both math and coding tasks. 58 | 59 | - All homeworks must be submitted to Gradescope by their posted deadlines. 60 | - Each assignment will include detailed instructions on how to submit your work for grading. It is the student’s responsibility to read these carefully and ensure that their work is submitted correctly. Assignment accommodations will not be granted in cases where students have mis-submitted their work (for example, by submitting to the wrong portal, submitting only part of an assignment, or forgetting to select pages). 61 | - The primary form of support students will have for homeworks are **office hours** and **Ed**. 62 | 63 | ### Vitamins 64 | {:.no_toc} 65 | Vitamins are weekly short Gradescope assignments to check that you are keeping up with lectures. They will be released on Thursdays after lecture and due on Sundays. 66 | 67 | ### Exams 68 | {:.no_toc} 69 | 70 | There will be two midterms in this class: 71 | 72 | - Midterm I on **October 8th**, 7 PM - 9 PM 73 | - Midterm II on **November 14th**, 7 PM - 9 PM 74 | 75 | There will **not** be a final exam. 76 | 77 | All exams must be taken in-person. You must sit the midterms at the specified time: if you have a conflict, please contact course staff ASAP at [data102@berkeley.edu](mailto:data102@berkeley.edu). **We will not accept any conflicts after the drop deadline**. 78 | 79 | ### Final Project 80 | {:.no_toc} 81 | At the end of the semester, you will apply the knowledge you learned in this class on a real-world dataset to complete a final project. You will be working in groups of 4. 82 | 83 | More details will be announced on Ed closer to the end of term. 84 | 85 | 86 | ## Grading Policies 87 | 88 | ### Grading Scheme 89 | 90 | Grades will be assigned using the following weighted components: 91 | 92 | |**Category**| **Percentage** | **Details** | 93 | | -- | -- | -- | 94 | | Vitamins| 5% | Drop 2 lowest scores | 95 | | Homeworks | 20% | No drop; 5 slip days | 96 | | Labs | 15% | Drop 2 lowest scores 97 | | Midterm 1 | 20% | | 98 | | Midterm 2 | 20% | | 99 | | Final project| 20% | | 100 | 101 | #### Grading Criteria 102 | {:.no_toc} 103 | 104 | * Homework will be graded on completion and correctness. No assignment may be dropped, but we have a slip day policy (see below). 105 | * Lab assignments will be graded on completion and correctness, but all test cases for autograded questions will be public. Your two lowest lab scores will be dropped. 106 | * When submitting assignments on Gradescope, **you must match each page to the corresponding question on Gradescope**. If you fail to do so, you may not receive credit for your work! 107 | * A grading rubric and more details regarding the final project will be released later in the semester. 108 | 109 | ### Regrade Requests 110 | * After each assignment is graded, course staff will post the deadline for regrade requests for that assignment on Ed. 111 | * To ensure that our grading team is not overworked, **regrade requests for each assignment must be submitted before the deadline** (except in cases of emergencies). 112 | * Note: When you submit a regrade request, we will take a fresh look at the question, so it is possible that you will receive a grade lower that what you originally received. 113 | 114 | ### Slip Days 115 | 116 | Each student gets an extension budget of 5 total slip days. You can use the extension on homework assignments only (not lab assignments, vitamins, or the final project) during the semester. Some important notes on slip days: 117 | * **Do not plan to use your slip days**: we're providing them for unforeseen circumstances. 118 | * **Slip days are self-serve**: we'll apply them to your assignments automatically. 119 | * **Slip days are full days, not hours.** We round up, so if you are 1 hour late, then 1 slip day will be used. (Why? We’d rather you get some sleep and make an attempt to finish the assignment the next day instead of staying up to micromanage hours.) 120 | * **For HW5 (and HW5 only), you may only use 2 slip days.** 121 | * After you have used your slip-time budget, any assignment handed in late will be marked off 20% per day late (rounded up to the nearest integer number of days). 122 | * No assignment will be accepted more than 5 days late. 123 | 124 | ### Extenuating Circumstances 125 | 126 | We recognize that our students come from varied backgrounds and have widely-varying experiences. If you encounter extenuating circumstances at any time in the semester, please do not hesitate to let us know. The sooner we are made aware, the more options we have available to us to help you. 127 | 128 | For any circumstances that cannot be resolved via slip days and drops, please contact us at [data102@berkeley.edu](data102@berkeley.edu). Within two business days, a member of course staff will reach out to you and provide a space for conversation, as well as to arrange course/grading accommodations as necessary. 129 | 130 | We recognize that at times, it can be difficult to manage your course performance — particularly in such a huge course, and particularly at Berkeley’s high standards. Sometimes emergencies just come up (personal health emergency, family emergency, etc.). This policy is meant to lower the barrier to reaching out to us, as well as build your independence in managing your academic career long-term. So please do not hesitate to reach out. 131 | 132 | Note that extenuating circumstances do not extend to the following: 133 | - Logistical oversight, such as Datahub/Gradescope tests not passing, submitting only one portion of the homework, forgetting to save your notebook before exporting, submitting to the wrong assignment portal, or not properly tagging pages on Gradescope. It is the student’s responsibility to identify and resolve these issues in advance of the deadlines. 134 | - Workload-related issues. It is the student’s responsibility to manage their other coursework and extracurricular commitments. We will not grant accommodations for these cases; instead, please use drops or slip days to cushion these issues. 135 | - Requests made **after** the assignment deadlines. Please make sure to submit a request before the assignment is due. 136 | 137 | Finally, simply submitting a request does not guarantee you will receive an extension. Even if your work is incomplete, please submit before the deadline so you can receive credit for the work you did complete. 138 | 139 | 140 | ### DSP Accommodations 141 | 142 | If you are registered with the Disabled Students’ Program (DSP) you can expect to receive an email from us during the first week of classes confirming your accommodations. Otherwise, email [data102@berkeley.edu](mailto:data102@berkeley.edu). DSP students who receive approved assignment accommodations will have a 2-day extension on homeworks and 1-day extension on labs and vitamins. Please note that any extension, plus slip days, cannot exceed 5 days. 143 | 144 | You are responsible for reasonable communication with course staff. If you make a request close to the deadline, we can not guarantee that you will receive a response before the deadline. 145 | 146 | ### Collaboration and Academic Integrity 147 | 148 | Data science is a collaborative activity. While you may talk with others about the homework, we ask that you write your solutions individually. If you do discuss the assignments with others please include their names at the top of your notebook. Keep in mind that content from the homeworks and labs will likely be covered on both of the midterms. We will be following the campus policy on [Academic Honesty](https://teaching.berkeley.edu/statements-course-policies), so be sure you are familiar with it. 149 | 150 | As a member of the Berkeley community, we expect you to follow the Berkeley Honor Code: 151 | 152 | “As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.” 153 | 154 | 155 | ## Waitlist 156 | 157 | If you are on the waitlist, you should complete and submit all assignments as if enrolled: we will not offer any makeup assignments or extensions for waitlisted students. 158 | 159 | For all other enrollment related issues, please reach out to the [Data Science advisors](https://data.berkeley.edu/spring-2024-classes-enrollment-info), as instructors and staff do not manage enrollment into the class. 160 | 161 | 162 | ## Community Resources 163 | 164 | ### Device Lending Options 165 | {:.no_toc} 166 | Students can access device lending options through the Student Technology Equity Program [STEP program](https://studenttech.berkeley.edu/devicelending). 167 | 168 | ### Data Science Student Climate 169 | {:.no_toc} 170 | [Data Science Undergraduate Studies](https://data.berkeley.edu/academics/undergraduate-programs) faculty and staff are committed to creating a community where every person feels [respected, included, and supported](https://data.berkeley.edu/equity-inclusion). We recognize that incidents may happen, sometimes unintentionally, that run counter to this goal. There are many things we can do to try to improve the climate for students, but we need to understand where the challenges lie. If you experience a remark, or disrespectful treatment, or if you feel you are being ignored, excluded or marginalized in a course or program-related activity, please speak up. Consider talking to your instructor, but you are also welcome to contact Executive Director Christina Teller at [cpteller@berkeley.edu](mailto:cpteller@berkeley.edu) or report an incident anonymously through this [online form](https://docs.google.com/forms/d/e/1FAIpQLSfBwaUe7VMQz6VzkYFvf4KYwNSTve9iJlBSQyAmsXoSE0LnWw/viewform). 171 | 172 | ### Community Standards 173 | {:.no_toc} 174 | Ed is a formal, academic space. We must demonstrate appropriate respect, consideration, and compassion for others. Please be friendly and thoughtful; our community draws from a wide spectrum of valuable experiences. For further reading, please reference Berkeley’s [Principles of Community](https://diversity.berkeley.edu/principles-community) and the [Berkeley Campus Code of Student Conduct](https://ethics.berkeley.edu/code-conduct). 175 | --------------------------------------------------------------------------------