PSY 410/510: Data Science for Psychology
  • Syllabus
  • Schedule
  • Content
  • Assignments
  • Project
  • Resources

On this page

  • Course Schedule
  • Week 1: Getting Started
    • Session 1: Introduction & Setup
    • Session 2: Your First Visualization
  • Week 2: Data Transformation
    • Session 3: Data Transformation I
    • Session 4: Data Transformation II
  • Week 3: Working with Data
    • Session 5: Data Tidying
    • Session 6: Data Import
  • Week 4: Reproducibility & Visualization
    • Session 7: Quarto & Reproducibility
    • Session 8: Layers & Aesthetics
  • Week 5: Visualization & EDA
    • Session 9: Perception & Design
    • Session 10: EDA — Variation
  • Week 6: Exploratory Data Analysis
    • Session 11: EDA — Covariation
    • Session 12: Data Types Grab Bag
  • Week 7: Data Wrangling
    • Session 13: Strings, Factors & Text
    • Session 14: Joins
  • Week 8: Missing Data
    • Monday, May 18
    • Session 15: Missing Data
  • Week 9: Storytelling with Data
    • Monday, May 25
    • Session 16: Storytelling with Data
  • Week 10: Modeling & Wrap-up
    • Session 17: Correlation & Simple Regression
    • Session 18: Putting It All Together
  • Finals Week
    • Wednesday, June 10
  • Assignment Summary
  • Final Project Milestones

Schedule

Course Schedule

Below is the schedule for PSY 410/510: Data Science for Psychology.

NoteSchedule Notes
  • Readings can be completed before or after the class session — quizzes are due Sunday at 11:59 PM
  • R4DS = R for Data Science (2nd ed.)
  • 📋 = Assignment due (Sundays at 11:59 PM) | 📝 = Project milestone
  • Schedule is subject to change based on course needs

Week 1: Getting Started

Session 1: Introduction & Setup

Monday, March 30

  • Why data science for psychology? The replication crisis as motivation
  • Installing R and RStudio
  • RStudio interface, projects, and organizing your work
  • File and variable naming conventions

📖 Read: R4DS Ch 2: Workflow: basics | R4DS Ch 6: Workflow: scripts and projects

Session 2: Your First Visualization

Wednesday, April 1

  • Introduction to ggplot2
  • Grammar of graphics: aesthetics, geoms, facets
  • Making your first plots

📖 Read: R4DS Ch 1: Data visualization

TipAssigned

Assignment 1: Getting Started


Week 2: Data Transformation

Session 3: Data Transformation I

Monday, April 6

  • Working with rows: filter(), arrange()
  • Working with columns: select(), mutate()
  • The pipe operator

📖 Read: R4DS Ch 3: Data transformation (sections 3.1–3.4)

Important📋 Assignment Due

Assignment 1: Getting Started — Due Sunday, April 5 at 11:59 PM

Session 4: Data Transformation II

Wednesday, April 8

  • Working with groups: group_by(), summarize()
  • Combining operations
  • Code style best practices

📖 Read: R4DS Ch 3: Data transformation (section 3.5) | R4DS Ch 4: Workflow: code style

TipAssigned

Assignment 2: Data Transformation


Week 3: Working with Data

Session 5: Data Tidying

Monday, April 13

  • Tidy data principles
  • Reshaping data: pivot_longer(), pivot_wider()
  • Common tidying problems

📖 Read: R4DS Ch 5: Data tidying

Important📋 Assignment Due

Assignment 2: Data Transformation — Due Sunday, April 12 at 11:59 PM

Session 6: Data Import

Wednesday, April 15

  • Reading and writing CSV files
  • Working with Excel spreadsheets
  • Common import issues and solutions

📖 Read: R4DS Ch 7: Data import | R4DS Ch 20: Spreadsheets


Week 4: Reproducibility & Visualization

Session 7: Quarto & Reproducibility

Monday, April 20

  • Literate programming: code + narrative in one document
  • Quarto documents: YAML, markdown, code chunks
  • Inline code and output formats
  • Rendering to HTML and PDF

📖 Read: R4DS Ch 28: Quarto

TipAssigned

Assignment 3: Tidying, Import & Quarto

Session 8: Layers & Aesthetics

Wednesday, April 22

  • Geoms, stats, and position adjustments
  • Coordinate systems and scales
  • Choosing the right plot for your data

📖 Read: R4DS Ch 9: Layers


Week 5: Visualization & EDA

Session 9: Perception & Design

Monday, April 27

  • Visual perception principles
  • Color theory and accessibility
  • Psychology-specific visualizations: error bars, interaction plots, raincloud plots

📖 Read: TBD (supplementary materials on visual perception)

TipAssigned

Assignment 4: Visualization Deep Dive

Important📋 Assignment Due

Assignment 3: Tidying, Import & Quarto — Due Sunday, April 26 at 11:59 PM

Session 10: EDA — Variation

Wednesday, April 29

  • Exploring distributions
  • Identifying outliers and unusual values
  • Asking good questions of your data

📖 Read: R4DS Ch 10: Exploratory data analysis (sections 10.1–10.4)

Warning📝 Project Milestone

Final Project Proposal Due — Dataset selection, research questions, initial plan


Week 6: Exploratory Data Analysis

Session 11: EDA — Covariation

Monday, May 4

  • Exploring relationships between variables
  • Patterns and models
  • Hands-on: Explore a psychology dataset

📖 Read: R4DS Ch 10: Exploratory data analysis (sections 10.5–10.6)

TipAssigned

Assignment 5: Exploratory Data Analysis

Important📋 Assignment Due

Assignment 4: Visualization Deep Dive — Due Sunday, May 3 at 11:59 PM

Session 12: Data Types Grab Bag

Wednesday, May 6

  • Logical vectors: comparisons and Boolean operations
  • Working with numeric data: rounding, summarizing
  • Recoding and computing scale scores

📖 Read: R4DS Ch 12: Logical vectors | R4DS Ch 13: Numbers


Week 7: Data Wrangling

Session 13: Strings, Factors & Text

Monday, May 11

  • Basic string cleaning: str_to_lower(), str_trim(), str_replace()
  • Working with categorical variables (factors)
  • Light text analysis: tokenizing and counting open-ended survey responses

📖 Read: R4DS Ch 14: Strings (sections 14.1–14.3) | R4DS Ch 16: Factors

TipAssigned

Assignment 6: Data Types & Wrangling

Important📋 Assignment Due

Assignment 5: Exploratory Data Analysis — Due Sunday, May 10 at 11:59 PM

Session 14: Joins

Wednesday, May 13

  • Primary keys and foreign keys
  • Mutating joins: left_join(), inner_join(), etc.
  • Filtering joins: semi_join(), anti_join()

📖 Read: R4DS Ch 19: Joins

TipAssigned

Assignment 7: Joins & Missing Data


Week 8: Missing Data

Monday, May 18

WarningNo Class

Use this time to work on your final project draft!

Session 15: Missing Data

Wednesday, May 20

  • Implicit vs. explicit missing values
  • Patterns of missingness
  • Strategies for handling missing data in psychological research

📖 Read: R4DS Ch 18: Missing values

Important📋 Assignment Due

Assignment 6: Data Types & Wrangling — Due Sunday, May 17 at 11:59 PM

Warning📝 Project Milestone

Final Project Draft Due — Working code, preliminary visualizations


Week 9: Storytelling with Data

Monday, May 25

WarningNo Class — Memorial Day

Session 16: Storytelling with Data

Wednesday, May 27

Drawing from Knaflic’s “Storytelling with Data”

  1. Understand the context — Audience, message, and the Big Idea
  2. Choose appropriate visuals — Matching plot types to data and questions
  3. Eliminate clutter — Gestalt principles; strategic contrast
  4. Focus attention — Preattentive attributes (color, size, position)
  5. Think like a designer — Visual hierarchy; accessibility; colorblind-friendly palettes

Plus: Data integrity, the replication crisis, and why reproducible workflows matter

📖 Read: R4DS Ch 11: Communication

TipAssigned

Assignment 8: Storytelling Report

Important📋 Assignment Due

Assignment 7: Joins & Missing Data — Due Sunday, May 24 at 11:59 PM


Week 10: Modeling & Wrap-up

Session 17: Correlation & Simple Regression

Monday, June 1

  • Quantifying relationships: cor() and cor.test()
  • Introduction to lm(): fitting and interpreting a simple linear model
  • Connecting EDA to inference: “is this pattern real?”
  • A preview of what’s next in your stats courses

📖 Read: TBD (supplementary intro to correlation and regression in R)

Important📋 Assignment Due

Assignment 8: Storytelling Report — Due Sunday, May 31 at 11:59 PM

Session 18: Putting It All Together

Wednesday, June 3

  • Complete analysis workflow demonstration
  • Common errors and debugging strategies
  • Course review: What you’ve learned
  • Where to go from here: next steps for continued learning
  • Final project Q&A

📖 Read: R4DS Ch 8: Workflow: getting help

Warning📝 Project Milestone

Final Project Report Due — Complete Quarto report with visualizations


Finals Week

Wednesday, June 10

Warning📝 Final Presentations Due

5-minute recorded video presentations — due Wednesday, June 10 at 11:59 PM on Canvas


Assignment Summary

Assignment Topic Assigned Due
1 Getting Started Wed Apr 1 Sun Apr 5
2 Data Transformation Wed Apr 8 Sun Apr 12
3 Tidying, Import & Quarto Mon Apr 20 Sun Apr 26
4 Visualization Deep Dive Mon Apr 27 Sun May 3
5 Exploratory Data Analysis Mon May 4 Sun May 10
6 Data Types & Wrangling Mon May 11 Sun May 17
7 Joins & Missing Data Wed May 13 Sun May 24
8 Storytelling Report Wed May 27 Sun May 31

Final Project Milestones

Milestone Due Date
Proposal Session 10 (Apr 29)
Draft Session 15 (May 20)
Final Report Session 18 (Jun 3)
Presentation Wed Jun 10

Content 2026 by Sara Weston
All content licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0)

 

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