Welcome to MATH STAT 4380!

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# STAT 4380: Data Science

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## What is data science?

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

- Set of tools to help us be better **stewards of information**

- We're going to learn to do this in a `tidy` and reproducible way -- more on that later!

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## Course FAQ

**Q - What data science background does this course assume?**
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A - None. Pre-req: one stats class

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**Q - Is this a statistics course?**
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A - While statistics `\(\neq\)` data science, they are very closely related and have tremendous overlap. Hence, this course will help you utilize and further your statistical thinking. However this course is not your typical statistics course - we will emphasize working and thinking with data, not statistical inference or modeling.

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**Q - What computing language / software will we use?**
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A - R
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## Some of what you will learn / do

- Data visualization and wrangling with `ggplot2` and `dplyr` from the `tidyverse`

- Data science workflow and scientific practice

- Ask good questions of real-world data

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- Data communication / storytelling

- Data ethics

- Reproducible reports with `Quarto`

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## Examples of data science in practice

- [The World Bank - ATLAS of Sustainable Development Goals](https://datatopics.worldbank.org/sdgatlas/goal-1-no-poverty)

- [The Upshot: 11 Data Points and Discoveries That Stuck With Us in 2025](https://www.nytimes.com/2023/08/26/upshot/spelling-bee-words.html)

- [When Caitlin Clark Comes to Town](https://www.nytimes.com/2024/03/06/upshot/caitlin-clark-popularity-attendance.html)

- [The Eclipses Path is Also Leaving a Trail of High Hotel Prices](https://www.nytimes.com/2024/04/04/upshot/eclipse-hotel-prices.html)

- [Tracking their life via Fitbit](https://livefreeordichotomize.com/posts/2017-12-27-a-year-in-fitbit.Rmd/)

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# The Course

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## Course Toolkit

**Course Website:** [https://nova-stat-4380.netlify.app](https://nova-stat-4380.netlify.app)
  - Central hub for the course!

**Blackboard**: Gradebook, Submit assignments, Piazza

**RStudio:** Software -- more on this later!

**GitHub repo:** Assignment templates -- more on this later!

**Perusall:** Lecture videos + Community Reading Annotations (data ethics)

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## Course structure / assignments

- **Prepare**: lecture videos on Perusall (due Tuesday class time)

- **Tuesday class**: in-class practice, live coding, Q&A, Application Exercises due at end of class
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- **Thursday class**: lab assignments started in-class, due following Thursday

- **Weekly quizzes**: usually on Thursdays (first one next week)

- **Reading annotations**: engaged with assigned articles/videos/podcasts in Perusall by class-time on Thursdays

- **Course project**: scaffolded throughout the semester, analysis on data of your choosing, related to the broad theme of "data for social good"

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## Where to find help

- In-class!

- Attend (drop-in) Student Hours
  - Mondays 3 - 4:30pm (Zoom)
  - Tuesdays 1 - 2pm (my office, SAC 370)
  - Thursdays 11am - 12pm (my office, SAC 370)

- Use **Piazza** (linked on Blackboard) for any questions about course content and/or assignments, since other students may benefit from the response. And it's easier to type code and math, compared to email.

- Use email for questions regarding personal matters and/or grades.

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## Course community & Learning environment

*What do you need [from me, from yourself, from your peers] for this semester to be successful?*

Traditional vs. flipped learning

- Traditional classroom: first engagement with the material comes via a lecture in class, deeper engagement (e.g. homework) comes outside of class
- Flipped learning: first engagement with the material comes in "individual space", and "group space" is used for deeper engagement with the material, when instructor is still there to guide

If you have participated in flipped learning before, what was your experience? In what ways did it require a shift in your approach to class/learning?

If you have NOT participated in flipped learning, what is your first impression of the idea? In what ways might it require a shift in your approach to class/learning?

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## Class Norms

+ Everyone has expertise
+ Be present
+ Participate to the best of your ability
+ Share talk time
+ Critique ideas, not people
+ Engage new perspectives with curiosity, not judgement
+ Exhibit intellectual humility
+ Embrace discomfort & non-closure
+ Normalize time to think
+ Others??

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# Let's dive in!

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# Your turn!

- Complete 4 steps on [this page](https://kgfitzgerald.github.io/NOVA_STAT_4380/announcements/week-00.html) "Getting Started with R & RStudio"

- Application Exercise (AE) 01a

- Download the AE-01a-unvotes.qmd file
    - Place it in your STAT_4380 folder on your computer
    - Back in RStudio, open the .qmd file by clicking on it in the Files quadrant (bottom right)
    - Complete the tasks in the "Try it out!" section

#### For Thursday: Watch "Meet the Toolkit" lecture in Perusall

#### For Saturday (this week only): Complete Community Reading Annotations (3+ thoughtful comments)