**Tuesday**| 2019.1.8- First day of class! Syllabus discussion and course overview. Maybe some
`R`

discussion.

- First day of class! Syllabus discussion and course overview. Maybe some
**Thursday**| 2019.1.10- Presentation by the OSU Advancement Office, this semester’s data partner.

**Tuesday**| 2019.1.15- How much R do we remember? Some brief remarks on data ethics.

**Thursday**| 2019.1.17- Begin discussion of practice project.

**Friday**| 2019.1.18**Deadline:**Reading Quiz

**Tuesday**| 2019.1.22- Work on practice projects

**Thursday**| 2019.1.24- Project presentations
**Deadline:**Project Presentation

**Friday**| 2019.1.25**Deadline:**Project Summary**Deadline:**Project RMarkdown

**Tuesday**| 2019.1.29- Discussion of practice projects

**Thursday**| 2019.1.31- Introduce first midterm project

**Tuesday**| 2019.2.5- Group meetings with data provider and project work time.

**Thursday**| 2019.2.7- Group meetings with Dave and project work time.

**Tuesday**| 2019.2.12`caret`

overview. Project work time.

**Thursday**| 2019.2.14- Project work time.

**Tuesday**| 2019.2.19- Project work time. Indroduction to second midterm project.
**Deadline:**Midterm Project I

**Thursday**| 2019.2.21- Project work time.

**Tuesday**| 2019.2.26- Group meetings with data provider and project work time.

**Thursday**| 2019.2.26- Project work time.

**Tuesday**| 2019.3.5- Project work time.

**Thursday**| 2019.3.7- Project work time.
**Deadline:**Midterm Project II Presentations (Documention due previous day.)**Deadline:**Midterm Project II RMarkdown (Due on Friday.)

**Tuesday**| 2019.3.12- No class. Spring Break!

**Thursday**| 2019.3.14- No class. Spring Break!

The following list are suggested readings, some of which will be assigned throughout the semester. This list will grow during the semester.

- Kass et al. - Ten Simple Rules for Effective Statistical Practice
- Breiman - Statistical Modeling: The Two Cultures
- Donoho - 50 Years of Data Science
- Box - Science and Statistics
- Wickham - Tidy Data
- Delgado et al. - Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
- Benjamin et al. - Redefine Statistical Significance
- Winston - How to Speak
- Domingos - A Few Useful Things to Know about Machine Learning

The following is an incomplete list of useful references which will be updates throughout the semester.

- Wickham and Grolemund - R for Data Science
- Dalpiaz - Applied Statistics with R
- Dalpiaz - R for Statistical Learning
- Wickham - Advanced R
- Burns - The R Inferno
- Gillespie and Lovelace - Efficient R Programming
- Xie, Allaire, and Grolemund - R Markdown: The Definitive Guide
- RStudio - RStudio Cheat Sheets
- Bryan, Hester - Happy Git and GitHub for the useR
- Bryan, Hester - WTF: What They Forgot to Teach You About R
- Misc. - R Weekly