Front Matter
Welcome to Data Science for Psychologists
- Mason Notes
  - How to use these notes
  - Status of course
Attribution
- Major Attributions
- Additional Attributions
License
Sitemap
Colophon
I Module 00
Don’t Miss Module 00
Assignment Guidance
- 0.6 Materials
- 0.7 Portfolio Instructions
  - 0.7.1 EDA as Practice
  - 0.7.2 Additional Ground Rules
II Module 01
1 Welcome to Data Science
- 1.1 Module Materials
  - 1.1.1 Estimated Video Length
2 What is Data Science?
3 ACT: UN voting
4 LEC: Meet our toolbox
5 ACT: Bechdel
6 ACT: Oh My Git! Version Control Challenge
7 LEC: Thoughtful Workflow
8 RDD: R basics and workflows
9 RDD: Quick Starting with Github
10 LAB: Hello R!
- About The Hello R Lab
- Lab Goals
11 Aloha R!
12 Zdravo R!
- Packages
- Data
- Exercises
- Stretch goal
III Module 02
13 Welcome to Data and Visualization
- 13.1 Module Materials
  - 13.1.1 Estimated Video Length
14 Exploratory Data Analysis
- 14.1 What is in a dataset?
  - 14.1.1 Why do we visualize?
- 14.2 What we learned
15 LEC: Visualizing data with ggplot2
- 15.1 Learning goals for ggplot2
- 15.2 ggplot2 and aesthetics
16 LEC: Visualizing numerical data
- 16.1 Looking at Data
- 16.2 More on visualizing numerical data
  - 16.2.1 What we learned about visualizing numerical data
17 LEC: Visualizing categorical data
18 ACT: Star Wars Activity
- 18.0.1 What we learned about visualizing categorical data
19 ODD: Basic care and feeding of data in R
20 RDD: More on GITing Started with Github
21 LAB: Global plastic waste
- Learning goals
- Getting started
  - Packages
  - Data
- Warm up
- Exercises
- Wrapping up
IV Module 03
22 Welcome to the tidyverse!
- 22.1 Module Materials
- 22.2 Estimated Video Length
23 LEC: Tidy data
- 23.1 Data structures in R
24 LEC: Grammar of data wrangling
- 24.1 Piping
25 RDD: Introduction to dplyr
26 LEC: Hands on Data Wrangling
27 LEC: Working with multiple data frames
- 27.1 Case Studies in Joining
28 ODD: Merges and Collaboration
29 LAB: Nobel laureates
V Module 04
30 Welcome to Data Diving with Types
- 30.1 Module Materials
- 30.2 Estimated Video Length
31 LEC: Data types and recoding
- 31.1 Why should you care about data types?
- 31.2 Data types
32 ACT: Another Hotels
33 LEC: Special Values
- 33.1 Data classes
34 LEC: Working with factors
35 ACT: (An) Another Hotels
36 LEC: Working with Dates
37 LEC: Importing data
38 ODD: Writing and reading files
39 ACT: Data Import
40 ODD: Data Transformations and Tukey’s Ladder of Powers
41 LAB: Visualizing spatial data
VI Module 05
42 Welcome to Tips for Effective Data Visualization
- 42.1 Module Materials
- 42.2 Estimated Video Length
43 LEC: Designing effective visualizations
- 43.1 Principles for effective visualizations
44 LEC: Deeper Diving into ggplot2
45 RDD: Plots Behaving Badly: Lessons in Data Misrepresentation
46 ODD: Design choices in data visualization
47 ODD: Secrets of a happy graphing life
48 ODD: Writing figures to file
49 LAB: Wrangling spatial data
VII Module 06
50 Welcome to Confounding and Communication!
- 50.1 Module Materials
- 50.2 Video Length
51 LEC: Scientific studies and confounding
52 LEC: Communicating data science results effectively
53 LAB: Ugly charts and Simpson’s paradox
VIII Module 07
54 Welcome to web scraping and APIs!
- 54.1 Module Materials
- 54.2 Estimated Video Length
55 LEC: Scraping the web
56 ODD: Data usually finds me
57 LEC: Use API-wrapping packages
58 ODD: DIY web data
59 LAB: Better Viz
IX Module 08
60 Welcome to Functions and Automation
- 60.1 Module Materials
61 LEC: Functions
62 LEC: Automation
- 62.1 Code Along pt 3
- 62.2 Math to Coding
63 RDD: Write your own R functions
64 Enhancing the Function: Towards the ‘Perfectly Formed Rear-View Mirror’
65 Test on Unexpected Inputs
66 Function-writing practicum
67 LAB: University of Edinburgh Art Collection
X Module 09
68 Welcome to Data and Ethics
- 68.1 Module Materials
69 Data Science and Ethics
70 LEC: Bias
- 70.1 Curated Videography
- 70.2 Annotated Bibliography Instructions
71 LEC: Society and AI
- 71.1 Curated Videography
  - 71.1.1 Last Week Tonight with John Oliver
72 LAB: Ethics in Data Science
XI Module 10
73 Welcome to modeling the tidy way!
- 73.1 Module Materials
74 Language of Models
- 74.1 What is a model?
- 74.2 Modeling the relationship between variables
75 LEC: Fitting and interpreting models
- 75.1 Models with numerical explanatory variables
- 75.2 A More Technical Worked Example
76 Models with FOO
- 76.1 Models with categorical explanatory variables
- 76.2 Modeling non-linear relationships
77 Modeling with multiple predictors
78 ODD: Notes on Logistic Regression
- 78.1 Predicting categorical data
- 78.2 Sensitivity and specificity
79 LAB: Modeling professor attractiveness and course evaluations
XII Module 11
80 Welcome to Overfitting and Cross-Validation
- 80.1 Module Materials
81 LEC: Overfitting
- 81.1 Prediction
- 81.2 Workflow
82 LEC: Cross-Validation
- 82.1 V-Fold
83 ODD: Feature Engineering
84 ODD: Notes on Cross validation
85 LAB: Cross Validation in Action
XIII Module 12
86 Welcome to Quantifying Uncertainty
- 86.1 Module Materials
87 Quantifying Uncertainty
88 Bootstrapping
89 ODD: Notes on Hypothesis Testing
90 LAB: So what if you smoke when pregnant?
XIV Module 13
91 Welcome to Base R and Simulating Data
- 91.1 Module Materials
- 91.2 Estimated Video Length
92 LEC: Getting started with simulating data in R
93 ODD: Getting Started with Data Simulations in R
94 LAB: Simulating data
XV Module 14
95 Welcome to Large Language Models
- 95.1 Module Materials
- 95.2 Estimated Video Length
96 LEC: What are Large Language Models?
- 96.1 Data Science and LLMs
97 LEC: Applications of Large Language Models in Data Science
- 97.1 Use Cases in Data Science
  - 97.1.1 R Example: Text Classification (Sentiment Analysis)
  - 97.1.2 Text Generation (Simple Markov Chain)
98 ACT: Working with OpenAI’s API
99 Install packages if needed
100 install.packages(c(“httr”,“jsonlite”,“tidyverse”,“tidytext”))
101 API key management: read from environment variable
102 OpenAI Chat Completions wrapper
103 Helper to extract the content of the first choice
104 A tiny test call (one meme description)
105 Two prompt designs per meme (A and B)
106 Inspect
107 Generate captions for all meme-design combos
108 We’ll collect a tibble with: meme_id, format, description, design, caption
109 Quick preview
110 Lexical diversity by design
111 Sentiment proxy by design
- 111.1 Best Practices
- 111.2 Conclusion
XVI Module 15
112 Welcome to interactive web apps
- 112.1 Module Materials
113 RShiny Overview
114 Practical Advice from the Data Professor
115 LEC: All the Shiny things
116 Shiny Resources
- 116.1 Awesome add-on packages to Shiny
XVII Module 16
117 Special Topics: Reproducible reports
- 117.1 Module Materials
118 LAB: Efficient Workflow with R Projects and R Markdown
119 Basic Syntax
120 Child Documents
- 120.1 Extract and Run R-Code from R Markdown Files
  - 120.1.1 R Code
- 120.2 Your Turn
121 Parameterized Reports
XVIII Module 17
122 Special Topics: Machine, Learn
- 122.1 Module Materials
123 Neural Networks
- 123.1 What is a Neural Network?
- 123.2 How does it learn?
  - 123.2.1 Teaching A.I. to Play My Game
  - 123.2.2 Stickman A.I. Learns To Walk
124 Natural Language Processing
XIX Module Last
Don’t Miss The Last Module
- 124.1 Important Wake Forest Stuff
- 124.2 What Next?
  - 124.2.1 Industry Transition Stories
XX Workshop
Workshop Links
125 LAB: Optional Lab
126 LAB: Academic Freedom
- Learning goals
- Getting started and warming up
XXI Back Matter
127 Good Resources
- 127.1 Cheatsheets
128 Media without a home yet
129 R Commands
References
License: CC-BY-SA

Data Science for Psychologists

107 Generate captions for all meme-design combos