Course Overview

• Why learn R programming?

• How this course fits into the Data Science part of the program curriculum

• Skills you will acquire

Today’s Agenda

• What is Data Science?

• Why programming matters

• Our toolchain: R & RStudio

• Course logistics

What is Data Science?

• Extracting knowledge from data

• Interdisciplinary—statistics, computing, domain expertise

• Drives evidence‑based decisions

The Data Science Life‑Cycle

• Ask a question

• Collect/clean data Related Courses:

  • MIS 208: Introduction to Data Analysis
    
  • MIS 315/301: Data Analytics I

• Analyze/model Related Courses:

  • MIS 208: Introduction to Data Analysis
    
  • MIS 315/301: Data Analytics I
    
  • MIS 305: Data Analysis: Modelling
    
  • MIS 307: Decision Analysis I
    
  • MIS 314/302: Data Analytics II
    
  • MIS 306: Data Analysis: Forecasting
    
  • MIS 403: Machine Learning for Business and Economics
    
  • MIS 412: Topics in Machine Learning for Business (E)
    
  • MIS 410: Text Analytics (E)
    

• Communicate insights Related Courses:

  • MIS 311: Data Visualization and Communication for Business (E)
    
  • MIS 407: Topics in Data Communication for Business (E)
    

• Act & iterate

Where Does Programming Fit?

• Automates repetitive tasks

• Enables reproducibility

• Scales analysis to large datasets

• Programming helps you learn

Scripting vs Point‑and‑Click

• GUI tools (Excel, SPSS) are intuitive but limited

• Scripts capture every step of analysis

• Easier to share & audit

Reproducible Research

• Same code ⇒ same result

• Facilitates collaboration

• Required by many journals & companies

The Beauty of Programming Languages

• Programming languages are languages.

• It’s just text – which gives you access to two extremely powerful techniques!!!

• Ctrl + C

• Ctrl + V

• In addition, programming languages are generally

  • Readable (IMO way easier than trying to figure what someone did in an )

  • Open (so you can it)

  • Reusable and reproducible (so you can reuse your code for similar problems and other people can get the same results as you easily)

  • Diff-able (version control is extremely powerful)

Source: Content in “The Beauty of Programming Languages” is from Hadley Wickham’s You Can’t Do Data Science in a GUI or download from this link

Some Popular Languages for Data Science

• R

• Python

• SQL

• Julia

• SAS / Matlab (legacy)

Why R?

Pros

• Powerful and flexible

• High level language (Interpreted Language)

• Great for data visualization

Why R

• R is free 😄

  • R is a freely available language and environment for statistical computing and graphics 😄, 📉
    
  • R is an open-source programming language
  • R has grown tremendously 📈. There are 22441 packages available (as of 2025 summer) on CRAN
    
  • It has a powerful integrated development environment (IDE, graphical user interface that allows one to write R code and view the results), RStudio, which is also free

• Easy to create reproducible analyses using R

  • Some (may be all) HWs and Assignments should be submitted as a reproducible documents
    
  • If you have reproducible documents from analysis, you don’t need to worry about copy-and-pasting issues
    
  • Reproducible documents means you can re-create documents without difficulties
    
  • It is possible to create data-driven-documents as a reproducible documents
    
  • data-driven-documents means documents change as data change. This leads to have reports generated automatically

• R makes collaboration easier (Rmarkdown and RData images)

• Getting help is much simpler

  • Google
    
  • Stack Overflow
    
  • …

Cons

• Steep learning curve

• No centralized support

• Slower for many tasks

Why RStudio?

• Free

• Integrated development environment (IDE)

• Great code editor with: code highlighting, code completion, debugging

• Makes R approachable

• Version control & project management tools

Business Impact of Programming

• Faster insights → competitive edge

• Custom analytics pipelines

• Fosters data‑driven culture

Example Question

“Does online advertising increase sales?”

Programmatic approach:

1. Read data

2. Clean data

3. pre-process

4. Build model

5. Test hypothesis

6. Communicate results

Learning Curve Expectations

• Weeks 1–4: Fundamentals

• Weeks 5–8: Basics of R commands

• Weeks 9–14: Control flow, functions, data objects

Installing R (Preview)

• Visit CRAN website (google “cran”)

• Download installer

• Run installer

• Follow the installation steps

Installing RStudio (Preview)

• Download free RStudio Desktop (google “RStudio” or “Posit” )

• Go to open source

• Select RStudio IDE

• Download RStudio Desktop

• Install

• Connects to existing R installation

Basic R Syntax Teaser

• <- assignment operator

• Functions use parentheses: mean(x)

• Everything is an object

Objects & Variables Teaser

• Atomic types: numeric, character, logical

• Containers: vector, list, data frame

Vectors Teaser

• c(1,3,7)

• Ordered collection of elements

• Vectorized operations: x + 1

• Element recycling (shorter vector elements reused)

Data Frames

• data.frame(Name=c(“John”,“Julia”), Age=c(33,26))

• Tabular structure like an Excel sheet

• Columns can be different object types

• Basis for tidy data (will see more in MIS 208)

Reading Data into R

• read.csv(), readxl::read_excel()

• Always inspect structure with str()

• Check for encoding & locale issues (text and date/time objects)

Community Resources teaser

• CRAN Task Views

• RStudio Community forum

• Stack Overflow (r tag)

• Cheat sheets & books

Coding Best Practices teaser

• Clear object names (sales_q1)

• Comment liberally (#why, not just what)

• Keep scripts modular

Summary

• Programming = heart of data science

• R + RStudio: our toolkit

• Next week: setting up your environment

Books

• R Programming for Data Science, Click Here to Access Free HTML Book

• R for Data Science, Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund (2e). Click Here to Access Free HTML Book

• R Markdown: The Definitive Guide, Click Here to Access Free HTML Book