For the final project, you will be assigned into groups and conduct an exploratory data analysis of the U.S. Department of Education’s
Chapter 23: Model basics
Section 23.4 – Formulas and model families: http://r4ds.had.co.nz/model-basics.html#formulas-and-model-families
Section 23.5 – Missing values: https://r4ds.had.co.nz/model-basics.html#missing-values-5
Section 23.6 – Other model families: https://r4ds.had.co.nz/model-basics.html#other-model-families
Introductory Statistics with Randomization and Simulation
Click here to download the textbook.
Chapter 5: Introduction to linear regression
From the beginning through to the end of section 5.1.4
Section 5.4.1
Chapter 22: Introduction to Model section
Chapter 23: Model basics
Section 23.1 – Introduction: https://r4ds.had.co.nz/model-basics.html
Section 23.2 – A simple model: https://r4ds.had.co.nz/model-basics.html#a-simple-model
Section 23.3 – Visualizing models: https://r4ds.had.co.nz/model-basics.html#visualising-models
For your third homework assignment, you will use statistical inference to answer a question about the National Survey of Family Growth, Cycle 6 dataset published by the National Center for Health Statistics.
Introductory Statistics with Randomization and Simulation
Click here to download the textbook.
Chapter 2: Foundation for inference
Chapter 4: Inference for numerical data
Introductory Statistics with Randomization and Simulation
Click here to download the textbook.
Chapter 2: Foundation for inference
Introduction to computational and data sciences supplemental book
Chapter 4: Representing distributions
Introduction: http://book.cds101.com/representing-distributions.html
Section 4.1 – Probability mass functions: http://book.cds101.com/probability-mass-functions.html
Section 4.2 – Cumulative distribution functions: http://book.cds101.com/cumulative-distribution-functions.html
Chapter 7: Exploratory data analysis
Collaborate with your assigned group members to turn your individual submissions for Homework 2 into a data exploration report.
On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Working with data in the Tidyverse (Due: Nov-05)
Tidy your data
Transform your data
On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Working with data in the Tidyverse (Due: Oct-29)
You will be completing the full course, Working with data in the Tidyverse, over the next two weeks. Chapter 3 of the course, “Tidy your data”, provides you with practice using the tidyr package. However, the lessons in chapter 3 assume you’re familiar with the content in chapters 1 and 2, so it’s best to complete those first.
Explore your data
Tame your data
From Working with data in the Tidyverse (Due: Nov-05)
These are the module exercises for week 10. If you complete the first two chapters and want to get started on chapters 3 and 4 now, you may do so.
Tidy your data
Transform your data
From chapter 12: from the beginning through to the end of section 12.5
For your second homework assignment, you will explore a dataset about the passengers on the Titanic, the British passenger liner that crashed into an iceberg during its maiden voyage and sank early in the morning on April 16, 1912.
On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Introduction to the Tidyverse
Collaborate with your assigned group members to turn your individual submissions for Homework 1 into a data exploration report.
On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Introduction to the Tidyverse
Your first major assignment is a set of exercises based around a single dataset called rail_trail, which will provide you with practice in creating visualizations using R and ggplot2.
On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Data Visualization with ggplot2 (Part 1)
Aesthetics
Geometries
On Datacamp, watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Data Visualization with ggplot2 (Part 1)
Mini-assignment on editing R Markdown files and saving to GitHub.
Mini-assignment to practice using RStudio to run code blocks in RMarkdown files and to create visualizations using ggplot2.
From chapter 3: sections 3.1 through 3.5
Introduction to computational and data sciences supplemental book
All of chapter 3 (short)
You should have received an invitation in your Mason email address to register and join DataCamp. If you have not done so already, complete the registration so that you can join the Introduction to Computational and Data Sciences class there. You are to watch the tutorial videos and complete the interactive coding challenges from the following lessons, which will let you practice and supplement this week’s content.
Important!
Your progress through these lessons is being tracked and completing them counts towards the Module exercises category of your grade.
From Introduction to R
Intro to basics
Vectors
From Reporting with R Markdown
Authoring R Markdown reports
Embedding Code
From Working with the RStudio IDE Part 1
From Working with the RStudio IDE Part 2
Introduction to computational and data sciences supplemental book
A module exercise about a data science study that used Twitter data to predict election outcomes.
All of chapter 1
Introductory Statistics with Randomization and Simulation
All of Chapter 1, except skip sections 1.3.5, all of 1.4, all of 1.5, skip 1.6.8.