Michael Gelman ([email protected]), Claremont McKenna College
Office hours:
- In person: Mo/We 1:00-2:00 PM Bauer 216
- Virtual: Sign up here
Tutor sessions (BC 22):
- Mo 06:00-08:00 PM - Oleksandr (Alex) Horban
- Th 08:00-10:00 PM - William DeForest
Textbook 1: Modern Data Science with R (1st edition)
Textbook 2: An Introduction to Statistical Learning
- Syllabus
- GitHub reference quick guide
- GitHub reference full guide
- Additional free resource: R for Data Science
- Test assignment (due 09/02)
- Problem Set 1 (due 09/12)
- Problem Set 2 (due 09/23)
- Team Project 1 (due 09/27)
- Example .md
- Problem Set 3 (due 10/04)
- Problem Set 4 (due 10/10)
- Problem Set 5 (due 11/10)
- Team Project 2 (due 11/20) [Description]
- Problem Set 6 (due 11/29)
- Final Project (due 12/16) [Description]
Monday (intro, GitHub, test assignment)
- before class:
- Try to set up R, RStudio, Git, GitHub account (See GitHub reference quick guide and See GitHub reference full guide)
- in class:
- day 1 slides: .Rmd .html
- continue setting up software
- test assignment
Wednesday (reproducibility, R Markdown)
- before class:
- complete test assignment and push both .rmd and .md files to GitHub.
- read MDSR Chapter 1 and Appendix D
- Start looking at PS 1
- in class:
Monday
- Labor day!!
Wednesday (R objects, R functions)
- before class:
- read MDSR Appendix sections B.4, B.5 and C.2
- read Grolemund/Wickham sections 20.2 Vector Basics, 20.3 Types of Vectors (focus on logical, numeric), and 20.5 Lists
- Reminder: PS 1 due on Monday
- in class:
Monday (ggplot2 graphics)
- before class:
- read MDSR sections 3.1 and 3.2. Section 3.3 contains some
dplyrwork that I will save for discussion in chapter 4. - read Grolemund/Wickham sections 3.1 - 3.5
- read MDSR sections 3.1 and 3.2. Section 3.3 contains some
- in class:
Wednesday (more ggplot2 and interactive graphics)
- before class:
- little more ggplot: read Grolemund/Wickham sections 3.6 - 3.10
- just read pages 324-325 in MDSR to get a feel for map projections. For now we will just be working with simple maps that only need lat/long and build-in map boundaries.
- quick read MDSR sections 11.1-11.3 in chapter 11 to get a "big picture" idea of some of the interactive graphing options in R.
- Start on PS 2
- in class:
Monday (Introduction to dplyr)
- before class:
- read MDSR sections 4.1 and 4.2
- in class: basic data wrangling with
dplyr
Wednesday (Work on Team Project 1)
- before class:
- Make sure you have your Team Project 1 partners
- in class:
- Work with partners on Team Project 1
- Ask any questions related to material up to this point
Monday (Joins in dplyr)
- before class:
- read MDSR section 4.3 and 4.4
- get started with PS 3
- in class:
Wednesday (Data intake)
- before class
- read MDSR sections 5.5.3 and 5.5.4 (we'll come back to the other sections after the exam)
- read Grolemund/Wickham chapter 16 - focus on sections 16.2 and 16.3.
- in class
Monday (tidy data: reshaping with gather and spread)
- before class:
- read MDSR sections 5.1-5.3
- in class:
Wednesday (Strings and regular expressions)
- before class:
- read Grolemund/Wickham chapter 14 on strings and regular expressions
- finish up homework 4 - due Monday
- to tackle problem 4 Q2, make sure to review the
lubridateexamples in the day 8 slides and WG section 16.2.2.
- to tackle problem 4 Q2, make sure to review the
- in class:
Monday (Iteration)
- before class:
- read MDSR section 5.4
- in class:
Wednesday
- before class:
- study for exam 1
- in class:
- take exam 1
Monday
- Fall Break!!
Wednesday
- No class!!
- Professor going to Belgium for a conference at the National Bank of Belgium. Let me know if you have any requests pics
Monday (Statistical Learning)
- before class:
- read ISLR section 2.1-2.2, 3.1
- in class:
Wednesday (Intro to Classifiers)
- before class:
- in class:
Monday (Logistic regression)
- before class:
- Read ISLR section 4.3
- Read MDSR section 8.4.4 on ROC curves
- in class:
Wednesday (Cross Validation)
- before class:
- Read ISLR section 5.1
- Read MDSR section 8.4.1 (10.3.2)
- Read this blog on statistical learning vs. machine learning
- in class:
Monday (Decision Trees)
- before class:
- Read MDSR section 8.2.1-8.2.3 (11.1.1)
- Read ISLR section 8.1
- in class:
Wednesday (Other classifiers)
- before class:
- Read MDSR section 8.2.4-8.2.5 (11.1.2-11.1.3)
- Read ISLR section 2.2.3 (
k-nn), 8.2.1-8.2.2 (bagging\random forest)
- in class:
Monday (k-means clustering)
- before class:
- Read ISLR section 10.3-10.3.1
- Read MDSR section 9.1,9.1.2 (12.1,12.1.2)
- in class:
Wednesday (Hierarchical clustering)
- before class:
- Read ISLR section 10.3.2
- Read MDSR section 9.1.1 (12.1.1)
- in class:
Monday (Networks Intro)
- before class:
- Read MDSR section 16.1-16.2 (20.1,20.2)
- in class:
- Final project proposal
- exam 2 explanation
- day 20 slides: .Rmd .html
- day 20 activity: .Rmd .md
Wednesday (Thanksgiving!)
- Prepare Thanksgiving meal
- Eat Thanksgiving meal
- Sleep
Monday (Networks Statistics)
- before class:
- Finish up
PS6
- Finish up
- in class:
- Team Project 2 results
- Discuss
Final Projectproposals - day 21 slides: .Rmd .html
- day 21 activity: .Rmd .md
Wednesday (Exam 2)
- before class:
- study for exam 2
- bring a calculator
- in class:
- take [exam 2]
Monday (Networks Activity)
- before class
- read MDSR 16.3 and 16.4 (20.3,20.4)
- read this article on the Game of Thrones network
- in class
Wednesday (Work on Final Project)
- before class:
- Start to make progress on Final Project
- in class:
- Work with partners on Final Project
- Fill out evaluations
- Celebrate end of classes!!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent