| Eren Bilen | |
|---|---|
| [email protected] | |
| Office | Rector North 1309 |
| Office Hours | M 4:30-5:30pm, T 3-4pm, W 9-10am |
| GitHub | ernbilen |
- Meeting day/time: T-Th 9:00-10:15am, Tome 120
- Office hours also available by appointment.
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% |
- 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
- QuantEcon
- Live question link