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Hello world!

What is data science?

• + = data science?
• + = data science?
• + + = data science?
• + + = data science?

.large[ Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. We’re going to learn to do this in a tidy way – more on that later!]

What is this course?

This course is an introduction to data science that is designed for psychologists. It emphasizes statistical thinking and best practices.

Q - What data science background does this course assume?
A - None.

Q - Is this an intro CS course?
A - Although statistics and computer science ≠ data science, they are very closely related and have tremendous overlap. Hence, this course is a great way to get comfortable with those topics. However this course is not your typical course.

Q - Will we be doing computing?
A - Yes.

Q - What computing language will we learn?
A - R.

Q: Why not language X?
A: We can discuss that remotely over ☕.

Where is this course?

.large[ .center[ DataScience4Psych.github.io/DataScience4Psych/]]

Wrapping up… Hello world!

Data in the wild

The US of Bey

.footnote[Brooke Watson, blog.brooke.science/posts/the-us-of-bey]

Punctuation in literature

.footnote[ Julia Silge, juliasilge.com/blog/punctution-literature]

Text analysis of Trump’s tweets

.footnote[David Robinson, varianceexplained.org/r/trump-tweets]

Greatest Twitter scheme of all time

.footnote[ gist.github.com/mine-cetinkaya-rundel/03d7516dea1e5f2613a5d71c28edb08d]

Voting patterns in the UN

minecr.shinyapps.io/unvotes

Wrapping up… Data in the wild

Course structure and policies

Logistics

• PSY 703: Data Science for Psychologists (Flipped Classroom)
• S. Mason Garrison (Green 438/Zoom)
• Office Hours: calendly.com/smasongarrison
• Tukey 🐱, Archie 😺, Annie 😾

Structure

• Interactive: Some lectures, lots of learn-by-doing
• Mondays: Face-to-Face Tutorials
• Wednesdays: Solidarity Sessions
• Bring your laptop! 💻
• Flipped Lectures: Pre-recorded lectures, weekly modules

Big Ideas

• Reproducibility
• Replication
• Robust Methods
• Really Nice Visualization
• R

Categories of Topics

• What
• How

.footnote[ adapted from: https://towardsdatascience.com/getting-started-with-generative-art-in-r-3bc50067d34b]

Learning Outcomes

• Use R to visualize and model many kinds of data
• Given a dataset: visualize, hypothesize, analyze, and communicate

Materials

• Textbook: R for Data Science (r4ds.had.co.nz)
• Coursenotes: datascience4psych.github.io/DataScience4Psych
• Software: R, RStudio, GitHub

Milestones

• Labs
• Portfolio

Contract Grading

• Based on effort
• More representative of scientific process
• Details in syllabus and course notes

Diversity & Inclusion

• Respectful of all identities
• Encouragement to speak up about classroom experiences

How to get help

• Course forum over direct messages
• Office hours for in-person support

Tips for asking questions

• Search before posting
• Describe context with concepts, not assignment numbers
• Be precise and code-formatted

Sharing/reusing code

• Cite sources of copied/inspired code
• Collaborate, but don’t write code for others

Wrapping Up…

Your Turn!

Voting patterns in the UN

Create a GitHub account

• Go to github.com and create an account

Tips for usernames: - Include your name - Be unique and timeless - Avoid programming keywords

Your Turn Option 1

• Visit: github.com/DataScience4Psych/ae01a_unvotes
• Open and Knit unvotes.Rmd
• Change name and countries

Your Turn Option 2

• Visit: github.com/DataScience4Psych/ae01b_covid
• Open and Knit covid.Rmd
• Change name and countries

Wrapping Up…