Math 7360 - Data Analysis - Fall 2023

Syllabus

Course Topics

This course provides an applied approach to the statistical analysis of datasets using the R software package.

Course Objectives

By the end of this course, you will be able to:

1. Proficiently apply statistical methods in the analysis of real datasets
2. Clearly present results from a data analysis project in both oral and written forms

Program-Level Outcomes

Proficiency with data analysis in R

Lecture and Practicals

Mon 2:00pm - 2:50pm @ Richardson Building 201 and Zoom link https://tulane.zoom.us/j/96594901794
Wed 2:00pm - 2:50pm @ Richardson Building 201 and Zoom link https://tulane.zoom.us/j/96594901794
Fri 2:00pm - 2:50pm Zoom link https://tulane.zoom.us/j/91892993821

Instructor

Xiang Ji
Office: Gibson 401D
Email: xji4@tulane.edu
Office hours: Friday lab sessions https://tulane.zoom.us/j/91892993821.

Course Webpage

https://tulane-math-7360-2023.github.io/

Grades

Grades will be based on the homework (70%) and the course project (30%).

Course Project timelines

• Form a team of two to work on the project.

• Find a dataset of interest to you.

• Turn in a brief one-page description by the end of week 3 of Sept. 8th. (points: 3/30)

• Submit a mid-term report (2 - 4 pages, no more than 4 please) by the end of week 12 of Nov. 10th. (7/30)

• Present your work to your peers week 15 and 16. (10/30)

• Submit a final report (4 - 8 pages, no more than 8 please) to xji4@tulane.edu via email by December 14.

• Submit code to your own private GitHub repository on the course GitHub organization by December 14. (Report + Code, 10/30)

• (Optional, 5 bonus points towards total grade for each individual in team) Make a GitHub page for your project and demo in final presentation.

Student Resources

There are no required books for this course. Below is a list of useful resources if you are interested:

1. Reproducible Research with R and RStudio by Christopher Gandrud: Amazon, GitHub repo.
2. Happy Git with R by Jenny Bryan.
3. R Markdown: The Definitive Guide by Yihui Xie, J.J. Allaire, and Garrett Grolemund.
4. R for Data Science by Hadley Wickham and Garrett Grolemund.
5. Advanced R by Hadley Wickham.
6. R Packages by Hadley Wickham.
7. Rcpp For Everyone by Masaki E. Tsuda.

ADA/Accessibility Statement

Any students with disabilities or other needs, who need special accommodations in this course, are invited to share these concerns or requests with the instructor and should contact Goldman Center for Student Accessibility: http://accessibility.tulane.edu (Links to an external site.)or 504.862.8433.

Code of Academic Conduct

The Code of Academic Conduct applies to all undergraduate students, full-time and part-time, in Tulane University. Tulane University expects and requires behavior compatible with its high standards of scholarship. By accepting admission to the university, a student accepts its regulations (i.e., Code of Academic Conduct (Links to an external site.) and Code of Student Conduct (Links to an external site.)) and acknowledges the right of the university to take disciplinary action, including suspension or expulsion, for conduct judged unsatisfactory or disruptive.

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