Introduction to DS002R

Foundations of Data Science

Jo Hardin

August 25, 2025

Agenda 8/25/25

Syllabus
What is Data Science?
Tools

Important

Listen to the full conversation of Not So Standard Deviations - Compromised Shoe Situation.

Important

I need your GitHub user name - please email it to me.

Course structure

bi-weekly HW (to GitHub + Gradescope)
bi-weekly quizzes
mini-projects
in-class activities / clickers
ethical considerations

Additional details

Canvas has all the links
1. course website – almost everything
2. class notes
3. Canvas page – solutions and assignments
no computers (tablets fine)
good communication
TidyTuesday

Syllabus

office hours
mentor sessions
anonymous feedback
dates for assignments

links to resources
HW grading
project information

Important

I need your GitHub user name - please email it to me.

Participation

For full participation points, you’ll need to share at least one R-tip-of-the day.

Beginning of class, I’ll ask if anyone has ideas
You share the tip
I’ll post the tip online

What is Data Science?

Data science lives at the intersection between statistics, computer science, and discipline knowledge. It is generally the process by which we gain insight from data.

V1.0 - Drew Conway

2010: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

V2.0 - Steve Geringer

2014: http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html

V3.0 - Writuparna Banerjee

2020: https://medium.com/@writuparnabanerjee/the-difference-in-the-career-options-in-data-science-data-scientist-vs-data-engineer-vs-data-33209d0ac880

V4.0 - Joel Grus

2013: https://posit.co/wp-content/themes/Posit/public/markdown-blogs/role-of-the-data-scientist/index.html?wtime=%7Bseek_to_second_number%7D

Data Science Overview

Based on https://www.effectivedatastorytelling.com/post/a-deeper-dive-into-lego-bricks-and-data-stories, original source: https://www.linkedin.com/learning/instructors/bill-shander

Data Science in DS 002R

DS workflow	in DS002R	beyond DS002R
data acquisition	web scraping, relational databases (SQL)	APIs
data exploration	wrangling, strings, regular expressions	natural language processing
data visualization	grammar of graphics	animations
data conclusions	iteration, permutation tests	predictive modeling, machine learning, AI
data communication	yes!	yes!

Data Science in the Wild

Data science extracts knowledge from within a particular domain of inquiry. Examples from Pomona!

Shannon Burns (Psychological Science and Neuroscience) uses data to understand brain processes of social communication.
Charlotte Chang (Environmental Analysis & Biology) uses data to study and improve earth stewardship.
Anthony (Tony) Clark (Computer Science) uses data to improve the safety and reliability of mobile robots.
Manisha Goel (Economics) uses data to understand how people’s identities shape the fortunes of businesses where they work.
Jun Lang (Asian Languages and Literatures) uses data to analyze (1) the intersection of language, gender, and society, and (2) second language acquisition and pedagogy.
Frank Pericolosi (Physical Education) uses data to improve his team’s chances on the field.
Ami Radunskaya (Mathematics) uses data to model tumor growth and treatment.
Yuqing Zhu (Neuroscience) uses data to figure out how a jumble of neurons becomes smart.

Learning goals

By the end of the course, you will be able to…

gain insight from data
gain insight from data, reproducibly
gain insight from data, reproducibly, using modern programming tools and techniques
gain insight from data, reproducibly (with literate programming and version control), using modern programming tools and techniques
ethically gain insight from data, reproducibly (with literate programming and version control), using modern programming tools and techniques

Activity

What problem or task would you like to investigate using data?
What would be hard about executing the project?
What are the potential ethical frameworks to consider?
How would you define success?

Toolkit: Computing

We use tools to do the things. But the tools are not the things.

Reproducible data analysis

Reproducibility checklist

What does it mean for a data analysis to be “reproducible”?

Short-term goals:

Are the tables and figures reproducible from the code and data?
Does the code actually do what you think it does?
In addition to what was done, is it clear why it was done?

Long-term goals:

Can the code be used for other data?
Can you extend the code to do other things?

Toolkit for reproducibility

Scriptability \(\rightarrow\) R
Literate programming (code, narrative, output in one place) \(\rightarrow\) Quarto
Version control \(\rightarrow\) Git / GitHub

R and Positron

R logo

R is an open-source statistical programming language
R is also an environment for statistical computing and graphics
It’s easily extensible with packages

Positron logo

Positron is a convenient interface for R called an IDE (integrated development environment), e.g. “I write R code in the Positron IDE”
Positron is not a requirement for programming with R, but it’s very commonly used by R programmers and data scientists

R vs. Positron

On the left: a car engine. On the right: a car dashboard. The engine is labelled R. The dashboard is labelled Positron.

R packages

Packages: Fundamental units of reproducible R code, including reusable R functions, the documentation that describes how to use them, and sample data¹
As of August 23, 2025, there are 22,553 R packages available on CRAN (the Comprehensive R Archive Network)²
We’re going to work with a small (but important) subset of these!

Tour: R + Positron

Tour recap: R + Positron

A short list (for now) of R essentials

Functions are (most often) verbs, followed by what they will be applied to in parentheses:

do_this(to_this)
do_that(to_this, to_that, with_those)

Packages are installed with the install.packages() function and loaded with the library function, once per session:

install.packages("package_name")
library(package_name)

tidyverse

tidyverse.org

The tidyverse is an opinionated collection of R packages designed for data science
All packages share an underlying philosophy and a common grammar

Quarto

Fully reproducible reports – each time you Render, the analysis is run from the beginning
Code goes in chunks
Narrative goes outside of chunks

Tour: Quarto

Tour recap: Quarto

Positron IDE with a Quarto document, source code on the left and output on the right. Annotated to show the YAML, a link, a header, and a code chunk.

Environments

Important

The environment of your Quarto document is separate from the Console!

Remember this, and expect it to bite you a few times as you’re learning to work with Quarto!

Environments

First, run the following in the console:

x <- 2
x * 3

All looks good, eh?

Then, add the following in an R chunk in your Quarto document

x * 3

What happens? Why the error?

How will we use Quarto?

Every assignment is an Quarto document.
You’ll always have a template Quarto document to start with.
The amount of scaffolding in the template will decrease over the semester.

Toolkit: Version control and collaboration

Git and GitHub

Git logo

Git is a version control system – like “Track Changes” features from Microsoft Word, on steroids
It’s not the only version control system, but it’s a very popular one

GitHub logo

GitHub is the home for your Git-based projects on the internet – like DropBox but much, much better
We will use GitHub as a platform for web hosting and collaboration (and as our course management system!)

Versioning - done badly

Versioning - done better

Versioning - done even better

with human readable messages

How will we use Git and GitHub?

Git and GitHub tips

There are millions of git commands – ok, that’s an exaggeration, but there are a lot of them – and very few people know them all. 99% of the time you will use git to add, commit, push, and pull.
We will be doing Git things and interfacing with GitHub through Positron, but if you Google for help you might come across methods for doing these things in the command line – skip that and move on to the next resource unless you feel comfortable trying it out.
There is a great resource for working with git and R: happygitwithr.com. Some of the content in there is beyond the scope of this course, but it’s a good place to look for help.