| Eren Bilen | |
|---|---|
| bilene@dickinson.edu | |
| Office | Rector North 1309 |
| Office Hours | M 4:30-5:30pm, T 3-4pm, W 9-10am |
| GitHub | ernbilen |
| Zoom Link | Click here |
- Meeting day/time: T-Th 10:30-11:45am, Tome 120
- Office hours also available by appointment.
- QRA: Chloe Ho hochl@dickinson.edu
- QRA Office Hours: T-F 1:30-2:30pm @Rector North 1311
Welcome to Data 180! This course provides an introduction to the core ideas of data science. Topics include data visualization, data wrangling, statistical measures of center, spread, and position, and supervised and unsupervised statistical/machine learning. Upon successful completion of the course a student will be able to
- Organize, manipulate, and transform data using R,
- Use Github and RMarkdown to create reproducible reports and maintain a repository for version control,
- Analyze and interpret data using visualization techniques and statistical summaries,
- Employ simple supervised and unsupervised machine learning techniques for predictive modeling,
- Identify internal structure in data organize, manipulate, and transform data in a statistical programming environment,
- Comprehend and create basic numerical and/or logical arguments.
We will make extensive use of the R and R-Studio to generate graphical and numerical representations of data, and apply basic machine learning techniques while we interpret the results. R is a fun and useful computational tool as well as an immediate resume builder!
Grades will be based on the categories listed below with the corresponding weights.
| Assignment | Points | Percent |
|---|---|---|
| Exam #1 | 20 | 20.0% |
| Exam #2 | 20 | 20.0% |
| Take-home Final | 20 | 20.0% |
| Homework | 40 | 40.0% |
| Total points | 100 | 100.0% |
- Accept my hw invitation link (this automatically creates a clone repo just for you)
- On this repo, hit Code -> Open with Github Desktop
- In Github Desktop -> hit Show in Finder (or explorer if you are on Windows)
- In your local Finder window, you will see the hw0 folder, go inside, work on the assignment.
- Once you are finished, save your changes in RStudio.
- Go back to Github Desktop, you will see that it recognized your changes in your local file, and it’s waiting for you to commit. Go ahead and commit (you must add a short comment at this stage about what changes you have made.)
- Push your changes by clicking on “Push origin” (blue button in the middle of Github Desktop window). You are done!
- R for Data Science by Hadley Wickham, Garrett Grolemund
- Introduction to Statistical Learning by Gareth James,Daniela Witten,Trevor Hastie, Robert Tibshirani
- Notes on Machine Learning & Artificial Intelligence by Chris Albon
- The Effect by Nick Huntington-Klein
- LaTeX Cheat Sheet and an excellent tutorial by Dave Richeson
- QuantEcon
- Live question link
- Eval link