COSC 375: Data Science

Spring 2024

Course Resources

General	Syllabus Modern Data Science with R: Our textbook, which provides a broad overview of the field using R as the language of choice. R: A programming language that excels at data analytics. RStudio: An IDE that is built for working in R and data science. DataCamp: An online platform for learning data science - you’ll need to create an account using your Wofford email address.
Data Science	Kaggle: A great website for finding and sharing datasets, joining competitions, etc. Regex Crossword: A set of games designed to help you learn and practice using regular expressions. RegExr: An online tool to learn, build, and test regular expressions. Data Science Prep: Get exceptionally good at data science interviews by getting real interview questions in your email inbox. Quarto: An open-source scientific and technical publishing system for creating dynamic content, authoring documents, publishing high-quality articles, reports, presentations, websites, blogs, and books in formats such as HTML, PDF, MS Word, and ePub, and authoring scientific markdown including equations, citations, crossrefs, figure panels, callouts, and advanced layout.
R / RStudio	CRAN Package List: A list of the 20,000+ R packages that can be installed using the install.packages() function. Posit Cheatsheet Collection: An excellent collection of printable cheatsheets on various R-based data science tools (RStudio, Quarto, ggplot2, shiny, etc.). Tidyverse: An opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. ggplot2 Documentation: A guide and reference on how to use the ggplot2 package in R. ggplot2 Extensions: Browse extensions that add additional functionality to ggplot2. Top 50 ggplot2 Visualizations: 50 chart examples with full R code provided. Color List: A list of color names that you can use in R. Custom Colors: A useful guide on how to use different color palettes (or your own colors) in ggplot2. Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles.
Books	Advanced R Beyond Multiple Linear Regression Data Science at the Command Line Efficient R Programming Engineering Production-Grade Shiny Apps Forecasting: Principles and Practice Fundamentals of Data Visualization Geocomputation with R Happy Git and GitHub for the useR Introduction to Probability Introduction to Probability for Data Science Introduction to R Markdown Introduction to Statistical Learning Mastering Shiny Modern Data Science with R Outstanding User Interfaces with Shiny R for Data Science R Graphics Cookbook R Packages Statistical Inference via Data Science Text Mining with R Tidy Modeling with R
Code Examples	Decision trees (tree.R) Submitting to a Kaggle competition (kaggle.R) k-Nearest Neighbors (knn.R) Random forests (rf.R) Naive Bayes (nb.R) k-Means (kmeans.R) Shiny (shiny_demo.R, shiny_demo2.R)

Course Schedule

Week	Date	Topics	Due
1	Feb 5th (Monday)	Course Introduction For next time: Read the syllabus, download and install R and RStudio, sign up on DataCamp using the invitation link, and start working on your first DataCamp assignment.
	Feb 7th (Wednesday)	The R Programming Language / RStudio For next time: Become familiar with R and RStudio. Your first DataCamp assignment is due Friday night.
	Feb 9th (Friday)	The R Programming Language / RStudio	DC: Introduction to R
2	Feb 12th (Monday)	Data Visualization with ggplot2
	Feb 14th (Wednesday)	Work Day Use this time to work on the DataCamp courses.	DC: Intermediate R Read Chapter 1
	Feb 16th (Friday)	Data Visualization with ggplot2	DC: Introduction to the Tidyverse
3	Feb 19th (Monday)	Data Transformation with dplyr	Read Chapter 2
	Feb 21st (Wednesday)	Data Transformation with dplyr	DC: Introduction to Data Visualization with ggplot2
	Feb 23rd (Friday)	Kaggle	DC: Intermediate Data Visualization with ggplot2
4	Feb 26th (Monday)	Visualizing with Color Note: Exam 1 is due March 6th. The exam is just completing any DataCamp Project that is in R and labeled as “guided”. Submit proof of completing it (such as a screenshot) to Moodle by the due date.	Read Chapter 3
	Feb 28th (Wednesday)	Strings and Regular Expressions with stringr	DC: Data Manipulation with dplyr
	Mar 1st (Friday)	Strings and Regular Expressions with stringr	DC: Joining Data with dplyr
5	Mar 4th (Monday)	Strings and Regular Expressions with stringr	Read Chapter 4
	Mar 6th (Wednesday)	Reproducible Reports with Quarto Note: Exam 1 is due tonight! Please make sure you have your proof of completion uploaded to Moodle by 11:59PM.	Exam 1: Any “Guided” R Project on DataCamp
	Mar 8th (Friday)	Reproducible Reports with Quarto	DC: Introduction to Importing Data in R
6	Mar 11th (Monday)	Dates and Times with lubridate
	Mar 13th (Wednesday)	Dates and Times with lubridate	DC: Introduction to Statistics in R
	Mar 15th (Friday)	Factors with forcats	DC: Data Communication Concepts
7	Mar 18th (Monday)	Databases with dbplyr
	Mar 20th (Wednesday)	Databases with dbplyr Note: No class on Friday. Please use this time to catch up (or get a head start) on some DataCamp courses.	DC: Working with Dates and Times in R
	Mar 22nd (Friday)	No Class (SIGCSE 2024)	DC: Cleaning Data in R
8	Mar 25th (Monday)	Machine Learning: Decision Trees
	Mar 27th (Wednesday)	Machine Learning: Kaggle Competitions	DC: Inroduction to Writing Functions in R
	Mar 29th (Friday)	Work Day	DC: Exploratory Data Analysis in R
9	Apr 1st (Monday)	No Class (Spring Holiday)
	Apr 3rd (Wednesday)	No Class (Spring Holiday)
	Apr 5th (Friday)	No Class (Spring Holiday)
10	Apr 8th (Monday)	Machine Learning: k-Nearest Neighbors (k-NN)
	Apr 10th (Wednesday)	Machine Learning: Random Forests with tidymodels Note: Exam 2 is due tonight! Please make sure you have your proof of completion uploaded to Moodle by 11:59PM.	Exam 2: Any “Unguided” R Project on DataCamp
	Apr 12th (Friday)	Machine Learning: Naive Bayes	DC: Introduction to Regression in R
11	Apr 15th (Monday)	Machine Learning: k-Means
	Apr 17th (Wednesday)	Shiny	DC: Intermediate Regression in R
	Apr 19th (Friday)	Shiny	DC: Supervised Learning in R: Classification
12	Apr 22nd (Monday)	Shiny Note: A reminder that Exam 3 is due 5/10. Please make sure you have your proof of completion uploaded to Moodle by 11:59PM on that date.
	Apr 24th (Wednesday)	Time Series	DC: Supervised Learning in R: Regression
	Apr 26th (Friday)		DC: Unsupervised Learning in R
13	Apr 29th (Monday)
	May 1st (Wednesday)		DC: Sampling in R
	May 3rd (Friday)		DC: Hypothesis Testing in R
14	May 6th (Monday)
	May 8th (Wednesday)		DC: Experimental Design in R
	May 10th (Friday)		Exam 3: Any “Guided” OR “Unguided” R Project on DataCamp
15	May 15th (Wednesday)	Final Presentations (11:30AM - 2:00PM)