General Student Resources
M.A. Help Room
The Department of Statistics offers a help-room service for MA Statistics students. The purpose of the help-room is to supplement and not replace the regular teaching assistant and faculty office hours with peer-to-peer tutoring.
Resources for R Programming
If you are looking to sharpen your programming skills we have listed below a number of resources that you might find helpful. Please be aware we do not monitor the quality of these resources, but we do hope that some of the information may be helpful to students.
- The R project for Statistical Computing: R-project website
- An Introduction to R (pdf): The official R manual, provided by its creators.
- Resources to help you learn and use R: Compiled by UCLA’s Technology Services
- The R Language Definition (pdf): A detailed guide to the technical terms of the R language. Useful to have when learning R from any source.
- R Programming Wikibook: A comprehensive source of information on R from introduction to more advanced topics.
- Penn 4-Week Summer R Course: A guided, 4-week tour of R.
- The R-Inferno (pdf): A guide to and description of trouble spots, odities, traps and glitches in R that may be a good resource once you’ve grown comfortable writing your first programs.
- An R and S-PLUS comopanion to Applied Regression: By John Fox and Sanford Weisberg. Webite includes code, data, and other resources used in the book.
- Bret Larget’s R Help
- A Brief Guide to R for Beginners in Econometrics by Mahmood Arai
- Using R for Data Analysis and Graphics: An introduction by J.H. Maindonald
- Kickstarting R by Jim Lemon
- Econometrics in R: By Grant V. Farnsworth
Online Videos/Courses for R Programming
- Computing for Data Analysis (Coursera): A 4-week, free online course that is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.
- Videos on Data Analysis with R: An introductory through advanced source of videos for the R student.
- Try R (CodeSchool): A free, interactive, introductory course for R beginners.
CU library has several resources you can make use of, like R workshops:
- All of the library’s past newsletters are available HERE
- Or go to library.columbia.edu > Locations > Science & Engineering Library and clicking on the link for Newsletters
Resources for Python
- Python Wiki – Beginner’s Guide: A textual introduction to Python by its creators.
- Instant Python: A crash-course in Python for those with programming experience (can already program in another language).
- Python Official Documentation: The complete Python information bank from its creators.
- Python Introduction, Resources and FAQ’s: Offered by Who is Hosting this?
Online Videos/Courses for Python
- Python (Codecademy): A thorough, interactive introduction to Python.
- Google’s Python Class (Google): A free class for people with a little bit of programming experience.
- A Gentle Intro to Programming Using Python (MIT OpenCourseWare): A beginner programming course that integrates Python into the curriculum.
- Quantitative Economics: This website presents a series of free lectures on quantitative economic modeling, designed and written byThomas J. Sargent and John Stachurski. The primary programming language is Python.
- Kaggle – hosts free problems for hundreds of universities around the globe. Engages students with an opportunity to apply machine learning to real problems.
- Stack Overflow – community of programmers where you can ask questions and look for jobs.
Private Tutoring by Columbia Mathematics Graduate Students
There are Math graduate students that offer tutoring services on a private, one-on-one basis. They tend to have significant tutoring experiences and the hourly fees vary from student to student, depending on the number of hours needed and the topics to be covered. If you are interested in finding out more, please review more information here. Please note that the Department of Mathematics, the Department of Statistics, and Columbia University bear no responsibility for the tutoring services, their content, or outcome.