Welcome to DSC365: Introduction to Data Science!

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.title[
# Welcome to DSC365: Introduction to Data Science!
]
.author[
### Alison Kleffner
]
.date[
### August 19, 2025
]

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## Homework

- Software downloaded by beginning of class on **Thursday August 21**
  + 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*

**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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.center[
## 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.

.center[
<img src="../images/data-science.png" width="60%" />
]

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### Data Science

.center[
<img src="../images/data-flowchart.png" width="1477" />
]

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.center[
## 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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.center[
## 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.

.pull-left[
- Breathe
- Mistakes are ok!
- Ask for help, Google is your friend!
- Translate code into plain English
- Don't reinvent the wheel
- Walk away

].pull-right[
<img src="../images/begin-R.png" width="1565" />
.center[Artwork by Allison Horst]
]

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### What is R?

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

- 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.

- “Integrated Development Environment”
- Clean user interface

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### Implication of Open-Source Software

Because *R* is open source, 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

**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