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 Lecture: Thoughtful Workflow
7 Notes: R basics and workflows
8 RDD: Quick Starting with Github
9 Lab: Hello R!
- About The Hello R Lab
- Lab Goals
10 Aloha R!
11 Zdravo R!
- Packages
- Data
- Exercises
III Module 02
12 Welcome to Data and Visualization
- 12.1 Module Materials
  - 12.1.1 Estimated Video Length
13 Exploratory Data Analysis
- 13.1 What is in a dataset?
  - 13.1.1 Why do we visualize?
14 Visualizing data with ggplot2
- 14.1 ggplot2 and aesthetics
15 Visualizing numerical data
- 15.1 Looking at Data
- 15.2 More on visualizing numerical data
16 Visualizing categorical data
17 Star Wars Activity
18 Basic care and feeding of data in R
19 RDD: More on GITing Started with Github
20 Lab: Global plastic waste
- Learning goals
- Getting started
  - Packages
  - Data
- Warm up
- Exercises
- Wrapping up
IV Module 03
21 Welcome to the tidyverse!
- 21.1 Module Materials
- 21.2 Estimated Video Length
22 Lecture: Tidy data
- 22.1 Data structures in R
23 Lecture: Grammar of data wrangling
- 23.1 Piping
24 Introduction to dplyr
25 Hands on Data Wrangling
26 Working with multiple data frames
- 26.1 Case Studies in Joining
27 ODD: Merges and Collaboration
28 Lab: Nobel laureates
V Module 04
29 Welcome to Data Diving with Types
- 29.1 Module Materials
- 29.2 Estimated Video Length
30 Data types and recoding
31 Importing data
32 Writing and reading files
33 ODD: Data Transformations and Tukey’s Ladder of Powers
34 Lab: Visualizing spatial data
VI Module 05
35 Welcome to Tips for Effective Data Visualization
- 35.1 Module Materials
- 35.2 Estimated Video Length
36 Designing effective visualizations
- 36.1 Principles for effective visualizations
37 Deeper Diving into ggplot2
38 Plots Behaving Badly: Lessons in Data Misrepresentation
39 ODD: Design choices in data visualization
40 ODD: Secrets of a happy graphing life
41 Writing figures to file
42 Lab: Wrangling spatial data
VII Module 06
43 Welcome to Confounding and Communication!
- 43.1 Module Materials
- 43.2 Video Length
44 Scientific studies and confounding
45 Communicating data science results effectively
46 Lab: Ugly charts and Simpson’s paradox
VIII Module 07
47 Welcome to web scraping
- 47.1 Module Materials
- 47.2 Estimated Video Length
48 Lecture: Scraping the web
49 Data usually finds me
50 Use API-wrapping packages
51 DIY web data
52 Lab: Better Viz
IX Module 08
53 Welcome to Functions and Automation
- 53.1 Module Materials
54 Functions
55 Automation
- 55.1 Code Along pt 3
- 55.2 Math to Coding
56 Write your own R functions
57 Enhancing the Function: Towards the ‘Perfectly Formed Rear-View Mirror’
58 Test on Unexpected Inputs
59 Function-writing practicum
60 Lab: University of Edinburgh Art Collection
X Module 09
61 Welcome to Data and Ethics
- 61.1 Module Materials
62 Data Science and Ethics
63 Bias
- 63.1 Curated Videography
- 63.2 Annotated Bibliography Instructions
64 Society and AI
- 64.1 Curated Videography
  - 64.1.1 Last Week Tonight with John Oliver
65 Lab: Modeling professor attractiveness and course evaluations
XI Module 10
66 Welcome to modeling the tidy way!
- 66.1 Module Materials
67 Language of Models
- 67.1 What is a model?
- 67.2 Modeling the relationship between variables
68 Fitting and interpreting models
69 Modeling non-linear relationships
70 Modeling with multiple predictors
71 Notes on Logistic Regression
- 71.1 Predicting categorical data
- 71.2 Sensitivity and specificity
72 Lab: Modeling with multiple predictors
XII Module 11
73 Welcome to Overfitting and Cross-Validation
- 73.1 Module Materials
74 Lecture: Overfitting
- 74.1 Prediction
- 74.2 Workflow
75 Lecture: Cross-Validation
- 75.1 V-Fold
76 Notes on Feature Engineering
77 ODD: Notes on Cross validation
78 Optional Lab
XIII Module 12
79 Welcome to Quantifying Uncertainty
- 79.1 Module Materials
80 Quantifying Uncertainty
81 Bootstrapping
82 Notes on Hypothesis Testing
83 Lab: So what if you smoke when pregnant?
XIV Module 13
84 Welcome to Base R and Simulating Data
- 84.1 Module Materials
- 84.2 Estimated Video Length
85 Lecture: Getting started with simulating data in R
86 Getting Started with Data Simulations in R
87 Lab: Simulating data
XV Module 14
88 Welcome to Large Language Models
- 88.1 Module Materials
- 88.2 Estimated Video Length
89 Lecture: What are Large Language Models?
- 89.1 Data Science and LLMs
90 Lecture: Applications of Large Language Models in Data Science
- 90.1 Use Cases in Data Science
  - 90.1.1 R Example: Text Classification (Sentiment Analysis)
  - 90.1.2 Text Generation (Simple Markov Chain)
91 Hands-on with OpenAI’s API
92 Lecture: Ethical Considerations of LLMs
XVI Module 15
93 Welcome to interactive web apps
- 93.1 Module Materials
94 RShiny Overview
95 Practical Advice from the Data Professor
96 All the Shiny things
97 Shiny Resources
- 97.1 Awesome add-on packages to Shiny
XVII Module 16
98 Special Topics: Reproducible reports
- 98.1 Module Materials
99 Efficient Workflow with R Projects and R Markdown
100 Basic Syntax
101 Child Documents
- 101.1 Extract and Run R-Code from R Markdown Files
  - 101.1.1 R Code
- 101.2 Your Turn
102 Parameterized Reports
XVIII Module 17
103 Special Topics: Machine, Learn
- 103.1 Module Materials
104 Neural Networks
- 104.1 What is a Neural Network?
- 104.2 How does it learn?
  - 104.2.1 Teaching A.I. to Play My Game
  - 104.2.2 Stickman A.I. Learns To Walk
105 Natural Language Processing
XIX Module Last
Don’t Miss The Last Module
- 105.1 Important Wake Forest Stuff
- 105.2 What Next?
  - 105.2.1 Industry Transition Stories
Thoughts from Hadley Wickham on Tidyverse
- 105.3 Dive into Hadley Wickham’s Tidyverse
- 105.4 Current State of the Tidyverse (2020)
XX Back Matter
106 Good Resources
- 106.1 Cheatsheets
107 Media without a home yet
108 R Commands
References
License: CC-BY-SA

Data Science for Psychologists

62 Data Science and Ethics

Data Science for Psychologists

62 Data Science and Ethics

62.1 Module Commentary

62.2 Misrepresenting Data

62.3 Maps