Front Matter
Welcome to Data Science for Psychologists
- Quick Start
- 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
1 RDD: Lily’s Quick Guide
- Lily Botha’s Quick Guide to Getting Started in Data Science for Psychologists
- Welcome to Data Science for Psychologists!
Assignment Guidance
- 1.1 Materials
- 1.2 Portfolio Instructions
  - 1.2.1 EDA as Practice
  - 1.2.2 Additional Ground Rules
II Module 01
2 Welcome to Data Science
- 2.1 Module Materials
  - 2.1.1 Estimated Video Length
3 LEC: What is Data Science?
4 ACT: UN voting
5 LEC: Meet our toolbox
6 ACT: Bechdel
7 ACT: Oh My Git! Version Control Challenge
8 LEC: Thoughtful Workflow
9 RDD: R basics and workflows
10 RDD: Quick Starting with Github
11 LAB: Hello R!
- About The Hello R Lab
- Lab Goals
12 Aloha R!
13 Zdravo R!
- Packages
- Data
- Exercises
- Stretch goal
III Module 02
14 Welcome to Data and Visualization
- 14.1 Module Materials
  - 14.1.1 Estimated Video Length
15 LEC: Exploratory Data Analysis
- 15.1 What is in a dataset?
  - 15.1.1 Why do we visualize?
- 15.2 What we learned
16 LEC: Visualizing data with ggplot2
- 16.1 Learning goals for ggplot2
- 16.2 ggplot2 and aesthetics
17 LEC: Visualizing numerical data
- 17.1 Looking at Data
- 17.2 More on visualizing numerical data
  - 17.2.1 What we learned about visualizing numerical data
18 LEC: Visualizing categorical data
19 ACT: Star Wars Activity
- 19.0.1 What we learned about visualizing categorical data
20 ODD: Basic care and feeding of data in R
21 RDD: More on GITing Started with Github
22 LAB: Global plastic waste
- Learning goals
- Getting started
  - Packages
  - Data
- Warm up
- Exercises
- Wrapping up
IV Module 03
23 Welcome to the tidyverse!
- 23.1 Module Materials
- 23.2 Estimated Video Length
24 LEC: Tidy data
- 24.1 Data structures in R
25 LEC: Grammar of data wrangling
- 25.1 Piping
26 RDD: Introduction to dplyr
27 LEC: Hands on Data Wrangling
28 LEC: Working with multiple data frames
- 28.1 Case Studies in Joining
29 ODD: Merges and Collaboration
30 LAB: Nobel laureates
V Module 04
31 Welcome to Data Diving with Types
- 31.1 Module Materials
- 31.2 Estimated Video Length
32 LEC: Data types and recoding
- 32.1 Why should you care about data types?
- 32.2 Data types
33 ACT: Another Hotels
34 LEC: Special Values
- 34.1 Data classes
35 LEC: Working with factors
36 ACT: (An) Another Hotels
37 LEC: Working with Dates
38 LEC: Importing data
39 ODD: Writing and reading files
40 ACT: Data Import
41 ODD: Data Transformations and Tukey’s Ladder of Powers
42 LAB: Visualizing spatial data
VI Module 05
43 Welcome to Tips for Effective Data Visualization
- 43.1 Module Materials
- 43.2 Estimated Video Length
44 LEC: Designing effective visualizations
- 44.1 Principles for effective visualizations
45 LEC: Deeper Diving into ggplot2
46 RDD: Plots Behaving Badly: Lessons in Data Misrepresentation
47 ODD: Design choices in data visualization
48 ODD: Secrets of a happy graphing life
49 ODD: Writing figures to file
50 LAB: Wrangling spatial data
VII Module 06
51 Welcome to Confounding and Communication!
- 51.1 Module Materials
- 51.2 Video Length
52 LEC: Scientific studies and confounding
53 LEC: Simpson’s Paradox
- 53.1 Introducing Simpson’s Paradox with a case study
- 53.2 Revisiting Simpson’s Paradox
54 ODD: Deeper into Simpson’s Paradox
55 LEC: Communicating data science results effectively
56 LAB: Ugly charts and Simpson’s paradox
VIII Module 07
57 Welcome to web scraping and APIs!
- 57.1 Module Materials
- 57.2 Estimated Video Length
58 LEC: Scraping the web
59 ODD: Data usually finds me
60 LEC: Use API-wrapping packages
61 ODD: DIY web data
62 LAB: Better Viz
IX Module 08
63 Welcome to Functions and Automation
- 63.1 Module Materials
64 LEC: Functions
65 LEC: Automation
- 65.1 Code Along pt 3
- 65.2 Math to Coding
66 RDD: Write your own R functions
67 RDD: Where were we? Where are we going?
68 ODD: Function-writing practicum
69 LAB: University of Edinburgh Art Collection
X Module 09
70 Welcome to Data and Ethics
- 70.1 Module Materials
71 LEC: Data Science and Ethics
72 LEC: Bias
73 ODD: Society and AI
- 73.1 Curated Videography
  - 73.1.1 Last Week Tonight with John Oliver
74 LAB: Ethics in Data Science
XI Module 10
75 Welcome to modeling the tidy way!
- 75.1 Module Materials
76 LEC: Language of Models
- 76.1 What is a model?
- 76.2 Modeling the relationship between variables
77 LEC: Fitting and interpreting models
- 77.1 Models with numerical explanatory variables
- 77.2 A More Technical Worked Example
78 LEC: Models with FOO
- 78.1 Models with categorical explanatory variables
- 78.2 Modeling non-linear relationships
79 LEC: Modeling with multiple predictors
80 ODD: Notes on Logistic Regression
- 80.1 Predicting categorical data
- 80.2 Sensitivity and specificity
81 LAB: Modeling professor attractiveness and course evaluations
XII Module 11
82 Welcome to Overfitting and Cross-Validation
- 82.1 Module Materials
83 LEC: Overfitting
- 83.1 Prediction
- 83.2 Workflow
84 LEC: Cross-Validation
- 84.1 V-Fold
85 ODD: Feature Engineering
86 ODD: Notes on Cross validation
87 LAB: Cross Validation in Action
XIII Module 12
88 Welcome to Quantifying Uncertainty
- 88.1 Module Materials
89 LEC: Quantifying Uncertainty
90 LEC: Bootstrapping
91 ODD: Notes on Hypothesis Testing
92 LAB: So what if you smoke when pregnant?
XIV Module 13
93 Welcome to Base R and Simulating Data
- 93.1 Module Materials
- 93.2 Estimated Video Length
94 LEC: Getting started with simulating data in R
95 RDD: Getting Started with Data Simulations in R
96 LAB: Simulating data
XV Module 14
97 Welcome to Large Language Models
- 97.1 Module Materials
- 97.2 Estimated Video Length
98 LEC: What are Large Language Models?
- 98.1 Data Science and LLMs
99 LEC: Applications of Large Language Models in Data Science
- 99.1 Use Cases in Data Science
  - 99.1.1 R Example: Text Classification (Sentiment Analysis)
  - 99.1.2 Text Generation (Simple Markov Chain)
100 ACT: Working with OpenAI’s API
XVI Module 15
101 Welcome to interactive web apps
- 101.1 Module Materials
102 LEC: RShiny Overview
103 ODD: Practical Advice from the Data Professor
104 LEC: All the Shiny things
105 Shiny Resources
- 105.1 Awesome add-on packages to Shiny
XVII Module 16
106 Special Topics: Reproducible reports
- 106.1 Module Materials
107 LAB: Efficient Workflow with R Projects and R Markdown
108 Basic Syntax
109 Parameterized Reports
XVIII Module 17
110 Special Topics: Machine, Learn
- 110.1 Module Materials
111 Neural Networks
- 111.1 What is a Neural Network?
- 111.2 How does it learn?
  - 111.2.1 Teaching A.I. to Play My Game
  - 111.2.2 Stickman A.I. Learns To Walk
112 Natural Language Processing
XIX Module Last
Don’t Miss The Last Module
- 112.1 Important Wake Forest Stuff
- 112.2 What Next?
  - 112.2.1 Industry Transition Stories
XX Workshop
Workshop Links
113 LAB: Optional Lab
114 LAB: Academic Freedom
- Learning goals
- Getting started and warming up
XXI Back Matter
115 Good Resources
- 115.1 Cheatsheets
116 Media without a home yet
117 R Commands
References
License: CC-BY-SA

Data Science for Psychologists

76 LEC: Language of Models

You can follow along with the slides here if you would like to open them full-screen.

76.1 What is a model?

76.2 Modeling the relationship between variables