Front Matter
Welcome to PSY 703
- 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
Guidance
- 0.6 Materials
- 0.7 Assignment Instructions
  - 0.7.1 Portfolio
  - 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 Lecture: Meet our toolbox
4 Bechdel Activity
5 Thoughtful Workflow
6 Notes: R basics and workflows
7 RDD: Quick Starting with Github
8 Lab: Hello R!
III Module 02
9 Welcome to Data and Visualization
- 9.1 Module Materials
  - 9.1.1 Estimated Video Length
10 Exploratory Data Analysis
- 10.1 What is in a dataset?
  - 10.1.1 Why do we visualize?
11 Visualizing data with ggplot2
- 11.1 ggplot2 and aesthetics
12 Visualizing numerical data
- 12.1 Looking at Data
- 12.2 More on visualizing numerical data
13 Visualizing categorical data
14 Star Wars Activity
15 Basic care and feeding of data in R
16 RDD: More on GITing Started with Github
17 Lab: Global plastic waste
- Learning goals
- Getting started
  - Packages
  - Data
- Warm up
- Exercises
- Wrapping up
IV Module 03
18 Welcome to the tidyverse!
- 18.1 Module Materials
- 18.2 Estimated Video Length
19 Lecture: Tidy data
- 19.1 Data structures in R
20 Lecture: Grammar of data wrangling
- 20.1 Piping
21 Introduction to dplyr
22 Hands on Data Wrangling
23 Working with multiple data frames
- 23.1 Case Studies in Joining
24 ODD: Merges and Collaboration
25 Lab: Nobel laureates
V Module 04
26 Welcome to Data Diving with Types
- 26.1 Module Materials
- 26.2 Estimated Video Length
27 Data types and recoding
28 Importing data
29 Writing and reading files
30 ODD: Data Transformations and Tukey’s Ladder of Powers
31 Lab: Visualizing spatial data
VI Module 05
32 Welcome to Tips for Effective Data Visualization
- 32.1 Module Materials
- 32.2 Estimated Video Length
33 Designing effective visualizations
- 33.1 Principles for effective visualizations
34 Deeper Diving into ggplot2
35 Plots Behaving Badly: Lessons in Data Misrepresentation
36 ODD: Design choices in data visualization
37 ODD: Secrets of a happy graphing life
38 Writing figures to file
39 Lab: Wrangling spatial data
VII Module 06
40 Welcome to Confounding and Communication!
- 40.1 Module Materials
- 40.2 Video Length
41 Scientific studies and confounding
42 Communicating data science results effectively
43 Lab: Ugly charts and Simpson’s paradox
VIII Module 07
44 Welcome to web scraping
- 44.1 Module Materials
- 44.2 Estimated Video Length
45 Lecture: Scraping the web
46 Data usually finds me
47 Use API-wrapping packages
48 DIY web data
49 Lab: Better Viz
IX Module 08
50 Welcome to Functions and Automation
- 50.1 Module Materials
51 Functions
52 Automation
- 52.1 Code Along pt 3
- 52.2 Math to Coding
53 Write your own R functions
54 Enhancing the Function: Towards the ‘Perfectly Formed Rear-View Mirror’
55 Test on Unexpected Inputs
56 Function-writing practicum
57 Lab: University of Edinburgh Art Collection
X Module 09
58 Welcome to Data and Ethics
- 58.1 Module Materials
59 Data Science and Ethics
60 Bias
- 60.1 Curated Videography
- 60.2 Annotated Bibliography Instructions
61 Society and AI
- 61.1 Curated Videography
  - 61.1.1 Last Week Tonight with John Oliver
62 Lab: Modeling professor attractiveness and course evaluations
XI Module 10
63 Welcome to modeling the tidy way!
- 63.1 Module Materials
64 Language of Models
- 64.1 What is a model?
- 64.2 Modeling the relationship between variables
65 Fitting and interpreting models
66 Modeling non-linear relationships
67 Modeling with multiple predictors
68 Notes on Logistic Regression
- 68.1 Predicting categorical data
- 68.2 Sensitivity and specificity
69 Lab: Modeling with multiple predictors
XII Module 11
70 Welcome to Overfitting and Cross-Validation
- 70.1 Module Materials
71 Lecture: Overfitting
- 71.1 Prediction
- 71.2 Workflow
72 Lecture: Cross-Validation
- 72.1 V-Fold
73 Notes on Feature Engineering
74 ODD: Notes on Cross validation
75 Optional Lab
XIII Module 12
76 Welcome to Quantifying Uncertainty
- 76.1 Module Materials
77 Quantifying Uncertainty
78 Bootstrapping
79 Notes on Hypothesis Testing
80 Lab: So what if you smoke when pregnant?
XIV Module 13
81 Welcome to Base R and Simulating Data
- 81.1 Module Materials
- 81.2 Estimated Video Length
82 Lecture: Getting started with simulating data in R
83 Getting Started with Data Simulations in R
84 Lab: Simulating data
XV Module 14
85 Welcome to Large Language Models
- 85.1 Module Materials
- 85.2 Estimated Video Length
86 Lecture: What are Large Language Models?
- 86.1 Basic Concepts of LLMs
- 86.2 Evolution of LLMs
87 Lecture: Applications of Large Language Models in Data Science
- 87.1 Use Cases in Data Science
  - 87.1.1 R Example: Text Classification (Sentiment Analysis)
  - 87.1.2 Text Generation (Simple Markov Chain)
88 Hands-on with OpenAI’s API
89 Lecture: Ethical Considerations of LLMs
XVI Module 15
90 Welcome to interactive web apps
- 90.1 Module Materials
91 RShiny Overview
92 Practical Advice from the Data Professor
93 All the Shiny things
94 Shiny Resources
- 94.1 Awesome add-on packages to Shiny
XVII Module 16
95 Special Topics: Reproducible reports
- 95.1 Module Materials
96 Efficient Workflow with R Projects and R Markdown
97 Basic Syntax
98 Child Documents
- 98.1 Extract and Run R-Code from R Markdown Files
  - 98.1.1 R Code
- 98.2 Your Turn
99 Parameterized Reports
XVIII Module 17
100 Special Topics: Machine, Learn
- 100.1 Module Materials
101 Neural Networks
- 101.1 What is a Neural Network?
- 101.2 How does it learn?
  - 101.2.1 Teaching A.I. to Play My Game
  - 101.2.2 Stickman A.I. Learns To Walk
102 Natural Language Processing
XIX Module Last
Don’t Miss The Last Module
- 102.1 Important Wake Forest Stuff
- 102.2 What Next?
  - 102.2.1 Industry Transition Stories
Thoughts from Hadley Wickham on Tidyverse
- 102.3 Dive into Hadley Wickham’s Tidyverse
- 102.4 Current State of the Tidyverse (2020)
XX Back Matter
103 Good Resources
- 103.1 Cheatsheets
104 Media without a home yet
105 R Commands
References
License: CC-BY-SA

Data Science for Psychologists

4 Bechdel Activity

You can find the materials for the Bechdel activity here. The compiled version should look something like the following…