Introduction to Statistical Computing¶
-- with R and Python
Feng Li
School of Statistics and Mathematics
Central University of Finance and Economics
The Evolution of Statistical Computing¶
Contents of this course¶
- Part I: Introduction to R/Python
- Part II: Statistical optimization and simulation
- Part III: Topics in Matrices and Data Mining
Pick up right tools¶
R is built by statisticians and leans heavily into statistical models and specialized analytics. Data scientists use R for deep statistical analysis, supported by just a few lines of code and beautiful data visualizations.
Python is a multi-purpose language, much like C++ and Java, with a readable syntax that is easy to learn. Programmers use Python to delve into data analysis or use machine learning in scalable production environments.
What shall I do: Pick up the right tool as you need.
Setup the environment¶
Installing R¶
Installing R on your own computer is simple and free.
If you use Microsoft Windows or macOS, visit http://www.r-project.org/. It shipped with a simple graphical user interface (GUI).
If you use Linux, search the phrase “r-cran” in your system’s software repository. Any text editor can be used to edit R source code.
RStudio is a good GUI front end for R on all platforms. https://posit.co/download/rstudio-desktop/ .
Installing Python¶
- Anaconda is popular open-source Python distribution. Download and install at https://www.anaconda.com/products/distribution .
Resources¶
We will cover some major R programming aspects in the first few lectures.
We also use Python and we assume you have some basic knowledge of it. If you need additional training, visit my Python course home page at https://feng.li/teaching/python/ .
Online course book (in Chinese) is available at https://feng.li/files/statscompbook/ .