Introduction to Statistical and Data Science Programming

This course is an introduction to programming for statistics and data science using both R and Python languages. Starting with basic programming concepts including variables, data types, data structures (lists, arrays, dictionaries, data frames, etc.), selection and repetition structures, to functions and packages, and finally data manipulation, exploration and visualization using both R and Python programming languages

Textbook References

• Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming https://ehmatthes.github.io/pcc_2e/regular_index/

• Python Data Science Handbook https://github.com/jakevdp/PythonDataScienceHandbook

• R for Data Science https://r4ds.had.co.nz/

• Learning R: A Step-by-Step Function Guide to Data Analysis 1st Edition https://cran.r-project.org/web/packages/learningr/

List of topics:

Part 1: Programming in Python

• Intro to Python 

• Variables, Simple Data Types, Lists

• If Statements, User Input, Dictionaries, While Loops

• Functions, Files

• Introduction to NumPy

• Data Manipulation with Pandas

• Visualization with Matplotlib

Part 2: Programming in R

• Intro to R, Data Types and Structures (Vectors, Matrices, Arrays, Lists, Data Frames). 

• Creating and calling functions, If Statements, and Basic Looping

• Installing R packages and Importing data

• Data visualization: ggplot2 and workflow basics

• Data transformation using tidyverse

• Workflow: scripts and exploratory data analysis

• Workflow: Projects and Tibbles

• Data Import and Tidy Data