Welcome to DSC365: Introduction to Data Science!

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## DSC365: Intro to Data Science
### Welcome to DSC365
#### January 13, 2026

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## Homework

- Software downloaded by beginning of class on **Thursday January 15**
  + Should hopefully have time at end of class

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## Alison Kleffner, PhD

- BS in Applied Mathematics and Economics at Rockhurst University
- Master and PhD in Statistics at University of Nebraska at Lincoln
- Thesis: *Visualization and Modeling of Multivariate Data in Environmental Applications*

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**Now let introduce yourselves! Get into groups of 2-3**

- Name
- Something your group has in common (besides things like major ...)

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## What is Data Science?
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### Data Science

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.

- Data science is a vast field, and there’s no way you can master it all by taking a single course. But we are going to lay a solid foundation.

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<img src="../images/data-science.png" alt="Venn Diagram depicting skills that a good data scientist must have. Data scientists needs skills from math and stats, computer science, and domain area." width="60%" />
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### Data Science

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<img src="../images/data-flowchart.png" alt="The Data Science Flow Chart. First you must import the data into your programing language of preference. Next you must clean the data. Then you have a cycle of transforming, visualizing, and modeling your data. Finally once you have answered your research question, you must then communicate your results to a non-statistical audience." width="1477" />
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## Syllabus and Course Specifics
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## What will be in this course?

- This is a programming course using *R*, the most commonly used *statistical* language
- We will briefly cover each part of the Data Science Cycle
- In this course you will complete in-class labs, projects, and presentations.
- Since this course involves using *R*, please bring your laptops to class with you

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## Syllabus

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## Software Set-Up
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### How to Learn a Programming Language

If you have never used *R* (or even heard of it) before, it is totally OK! We will be starting with the basics.

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- Breathe
- Mistakes are ok!
- Ask for help!
- Translate code into plain English
- Don't reinvent the wheel
- Walk away

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<img src="../images/begin-R.png" alt="Cartoon depicting that R can be frustrating to learn at first, but hopefully after some time it becomes more fun to use." width="1565" />
.center[<font size = "0.75">Artwork by Allison Horst</font>]
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### What is R?

*R* is a free software environment and programming language for statistical computing and graphics

- Created by Ross Ihaka and Robert Gentleman in 1993; Formally released by the [**R Core Group**](https://www.r-project.org/contributors.html) in 1997. 
- Open-source
- **Interpreted language**: we interact with R through a “command line” interpreter, which translates our “code” to machine code
- CRAN (comprehensive R archive network) is R's central software repository. Contains contributed packages
- New major release once a year

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### What is R Studio?

Every R installation comes with the *R* Console, so we don’t actually need an additional program to interface with R. BUT most of people who uses *R* ALSO uses RStudio to interact with it.

- History
  + **RStudio** was released in 2011 by J.J. Allaire.
  + They make money off the IDE and other helper software.
  + In 2020, RStudio became a PBC (*Public Benefit Corp*), meaning they are legally obligated to support education and open-source development.
- “Integrated Development Environment”
  + Clean user interface

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### Eventually... Move to Positron

From it's [website](https://positron.posit.co): "Positron unifies exploration and production work in one free, AI-assisted environment, empowering the full spectrum of data science in Python and R"

- Positron is under much more active development, with a rapidly expanding set of features. RStudio’s development, on the other hand, is much more focused on bug fixes and product stability.
- First stable release in July 2025 
  + I have been told there is still some things it needs to work through.

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### Implication of Open-Source Software

Because *R* is open source...

- No one company owns *R* (similar to Python)
- This means nobody can sell their **R** code!
  + But you can sell "helpers" like **RStudio**.
- Users can contribute *R* packages to add additional functions and capabilities (more than 19,000 as of March 2023). 
  + A complete list can be found [here](https://cran.r-project.org/web/packages/available_packages_by_name.html)
  + Pro: New statistical/data science techniques are added to CRAN, Bioconductor (another package repository), GitHub, etc. daily
  + Con: No standard syntax! Not all are well documented

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### Reproducible Research

Excerpt from the Simply Statistics blog: “The Real Reason Reproducible Research is Important” [(Source)](https://simplystatistics.org/posts/2014-06-06-the-real-reason-reproducible-research-is-important/)

- **Reproducible**: the original data (and original computer code) can be analyzed (by an independent investigator) to obtain the same results of the original study.
- Reproducibility is important because it is the only thing that an investigator can guarantee about a study.
- It does not necessarily ensure the results are correct, but does ensure transparency.

More reasons to do your programming in a reproducible way:

1. **Time saved**: especially important in the professional world
2. **Time elapsed**: even the best programmers forget how their code runs eventually

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### Reproducible Research with Quarto

Quarto provides “an authoring framework for data science”. In an Quarto document, you can:

- Save and execute code
- Generate written reports that can be shared with someone else
- Supported file formats: Word, PDF, HTML, slide shows, handouts, dashboards, Powerpoint

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**More on this next week**

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### Let's Install it Together

1. Download and run the R installer for your operating system from CRAN
  - Windows: https://cran.rstudio.com/bin/windows/base/
  - Mac: https://cran.rstudio.com/bin/macosx/
  - Linux: https://cran.rstudio.com/bin/linux/
2. Now download RStudio from the RStudio website – IDE of R
 - https://posit.co/download/rstudio-desktop/
3. If you are using a Mac, you may need to download XQuartz.
https://www.xquartz.org/
 - If you run into issues, try downloading this to see if it helps.

Go with all default options

**Other Option**: If you would prefer to not download it to your computer: https://posit.cloud

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### R Studio Set-Up

To Create a Script, Click the Following: File -> New File -> R script

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### R Studio Set-Up - Editor (Top Left)

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**Shortcuts for running code**: To run code from your script:
+ Windows: Ctrl + Enter
+ Mac: Cmd + Enter

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### R Studio Set-Up - Console (Bottom Left)

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### R Studio Set-Up - Environment  (Top Right)

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### R Studio Set-Up - Environment  (Bottom Right)

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