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
II Module 01
1 Welcome to Data Science
- 1.1 Module Materials
  - 1.1.1 Estimated Video Length
2 What is Data Science?
- 2.1 See for yourselves
- 2.2 Course structure and some other useful things
3 Activity: UN voting
- 3.1 UN Voting
- 3.2 COVID Data
4 Lecture: Meet our toolbox
5 Activity: Bechdel
6 Activity: Oh My Git! Version Control Challenge
7 Lecture: Thoughtful Workflow
8 Notes: 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
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?
15 Visualizing data with ggplot2
- 15.1 ggplot2 and aesthetics
16 Visualizing numerical data
- 16.1 Looking at Data
- 16.2 More on visualizing numerical data
17 Visualizing categorical data
18 Star Wars Activity
19 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 Lecture: Tidy data
- 23.1 Data structures in R
24 Lecture: Grammar of data wrangling
- 24.1 Piping
25 Introduction to dplyr
26 Hands on Data Wrangling
27 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 Data types and recoding
32 Importing data
33 Writing and reading files
34 ODD: Data Transformations and Tukey’s Ladder of Powers
35 Lab: Visualizing spatial data
VI Module 05
36 Welcome to Tips for Effective Data Visualization
- 36.1 Module Materials
- 36.2 Estimated Video Length
37 Designing effective visualizations
- 37.1 Principles for effective visualizations
38 Deeper Diving into ggplot2
39 Plots Behaving Badly: Lessons in Data Misrepresentation
40 ODD: Design choices in data visualization
41 ODD: Secrets of a happy graphing life
42 Writing figures to file
43 Lab: Wrangling spatial data
VII Module 06
44 Welcome to Confounding and Communication!
- 44.1 Module Materials
- 44.2 Video Length
45 Scientific studies and confounding
46 Communicating data science results effectively
47 Lab: Ugly charts and Simpson’s paradox
VIII Module 07
48 Welcome to web scraping
- 48.1 Module Materials
- 48.2 Estimated Video Length
49 Lecture: Scraping the web
50 Data usually finds me
51 Use API-wrapping packages
52 DIY web data
53 Lab: Better Viz
IX Module 08
54 Welcome to Functions and Automation
- 54.1 Module Materials
55 Functions
56 Automation
- 56.1 Code Along pt 3
- 56.2 Math to Coding
57 Write your own R functions
58 Enhancing the Function: Towards the ‘Perfectly Formed Rear-View Mirror’
59 Test on Unexpected Inputs
60 Function-writing practicum
61 Lab: University of Edinburgh Art Collection
X Module 09
62 Welcome to Data and Ethics
- 62.1 Module Materials
63 Data Science and Ethics
64 Bias
- 64.1 Curated Videography
- 64.2 Annotated Bibliography Instructions
65 Society and AI
- 65.1 Curated Videography
  - 65.1.1 Last Week Tonight with John Oliver
66 Lab: Modeling professor attractiveness and course evaluations
XI Module 10
67 Welcome to modeling the tidy way!
- 67.1 Module Materials
68 Language of Models
- 68.1 What is a model?
- 68.2 Modeling the relationship between variables
69 Fitting and interpreting models
70 Modeling non-linear relationships
71 Modeling with multiple predictors
72 Notes on Logistic Regression
- 72.1 Predicting categorical data
- 72.2 Sensitivity and specificity
73 Lab: Modeling with multiple predictors
XII Module 11
74 Welcome to Overfitting and Cross-Validation
- 74.1 Module Materials
75 Lecture: Overfitting
- 75.1 Prediction
- 75.2 Workflow
76 Lecture: Cross-Validation
- 76.1 V-Fold
77 Notes on Feature Engineering
78 ODD: Notes on Cross validation
79 Optional Lab
XIII Module 12
80 Welcome to Quantifying Uncertainty
- 80.1 Module Materials
81 Quantifying Uncertainty
82 Bootstrapping
83 Notes on Hypothesis Testing
84 Lab: So what if you smoke when pregnant?
XIV Module 13
85 Welcome to Base R and Simulating Data
- 85.1 Module Materials
- 85.2 Estimated Video Length
86 Lecture: Getting started with simulating data in R
87 Getting Started with Data Simulations in R
88 Lab: Simulating data
XV Module 14
89 Welcome to Large Language Models
- 89.1 Module Materials
- 89.2 Estimated Video Length
90 Lecture: What are Large Language Models?
- 90.1 Data Science and LLMs
91 Lecture: Applications of Large Language Models in Data Science
- 91.1 Use Cases in Data Science
  - 91.1.1 R Example: Text Classification (Sentiment Analysis)
  - 91.1.2 Text Generation (Simple Markov Chain)
92 Hands-on with OpenAI’s API
93 Lecture: Ethical Considerations of LLMs
XVI Module 15
94 Welcome to interactive web apps
- 94.1 Module Materials
95 RShiny Overview
96 Practical Advice from the Data Professor
97 All the Shiny things
98 Shiny Resources
- 98.1 Awesome add-on packages to Shiny
XVII Module 16
99 Special Topics: Reproducible reports
- 99.1 Module Materials
100 Efficient Workflow with R Projects and R Markdown
101 Basic Syntax
102 Child Documents
- 102.1 Extract and Run R-Code from R Markdown Files
  - 102.1.1 R Code
- 102.2 Your Turn
103 Parameterized Reports
XVIII Module 17
104 Special Topics: Machine, Learn
- 104.1 Module Materials
105 Neural Networks
- 105.1 What is a Neural Network?
- 105.2 How does it learn?
  - 105.2.1 Teaching A.I. to Play My Game
  - 105.2.2 Stickman A.I. Learns To Walk
106 Natural Language Processing
XIX Module Last
Don’t Miss The Last Module
- 106.1 Important Wake Forest Stuff
- 106.2 What Next?
  - 106.2.1 Industry Transition Stories
Thoughts from Hadley Wickham on Tidyverse
- 106.3 Dive into Hadley Wickham’s Tidyverse
- 106.4 Current State of the Tidyverse (2020)
XX Back Matter
107 Good Resources
- 107.1 Cheatsheets
108 Media without a home yet
109 R Commands
References
License: CC-BY-SA

Data Science for Psychologists

63 Data Science and Ethics

Data Science for Psychologists

63 Data Science and Ethics

63.1 Module Commentary

63.2 Misrepresenting Data

63.3 Maps