COSC 375: Data Science

Spring 2025

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 DataCamp: An online platform for learning data science - you’ll need to create an account using your Wofford email address
Upcoming Events	useR! 2025 Conference: An R conference that will be held at August 8th-10th that will be held at Duke University in Durham, North Carolina (registration opens March 3rd) The National Consortium for Data Science (Upcoming Events)
General Data Science	Data Science Prep: Get exceptionally good at data science interviews by getting real interview questions in your email inbox Kaggle: A great website for finding and sharing datasets, joining competitions, etc.
R / RStudio / IDEs	R: A programming language that excels at data analytics IDEs (Integrated Development Environments) RStudio: An IDE that is built for working in R and data science Positron: A next-generation data science IDE (it’s not yet finished, and is available in beta only) Visual Studio Code (VS Code): An IDE from Microsoft that is generalized to any type of programming language CRAN Package List: A list of the 20,000+ R packages that can be installed using the install.packages() function Tidyverse: An opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures Posit Cheatsheet Collection: An excellent collection of printable cheatsheets on various R-based data science tools (RStudio, Quarto, ggplot2, shiny, etc.) 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
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	Coming soon…

Course Schedule

Week	Date	Topics	Due
1	Feb 3rd (Monday)	Course Introduction For next time: Read the syllabus, download and install R and RStudio, sign up on DataCamp using the invitation link above, and start working on your first DataCamp assignment.
	Feb 5th (Wednesday)	The R Programming Language / RStudio For next time: Continue learning your way around RStudio, and play around with R. We will continue with more R next time. Finish your first DataCamp course by Friday night (11:59PM). You will usually have 3 data camp courses every two weeks.
	Feb 7th (Friday)	The R Programming Language / RStudio For next time: Continue learning your way around RStudio, and play around with R. Your first DataCamp course is due tonight, and your second course is due Wednesday night. They will always be due 11:59PM of their due date. We will move into data visualization next time.	DC: Introduction to R
2	Feb 10th (Monday)	Data Visualization with ggplot2 For next time: We will continue learning about data visualization using the ggplot2 package. Since we are going to be using several packages in the TidyVerse, I recommend installing the entire Tidyverse by running install.packages("tidyverse").
	Feb 12th (Wednesday)	Data Visualization with ggplot2 For next time: We will continue learning about data visualization using the ggplot2 package.	DC: Intermediate R Read Chapter 1
	Feb 14th (Friday)	Data Visualization with ggplot2 / Kaggle For next time: We will finish up the introduction to data visualization using ggplot2, and start moving into data wrangling with the dplyr package (which is part of the tidyverse).
3	Feb 17th (Monday)	Color / Data Transformation with dplyr For next time: We will continue learning about data wrangling using dplyr. Remember to keep up with the DataCamp courses.	DC: Introduction to the Tidyverse
	Feb 19th (Wednesday)	Data Transformation with dplyr For next time: We will continue learning about data wrangling using dplyr. Your next DataCamp course is due Friday night.	Read Chapter 2
	Feb 21st (Friday)	Data Transformation with dplyr	DC: Introduction to Data Visualization with ggplot2
4	Feb 24th (Monday)
	Feb 26th (Wednesday)		DC: Intermediate Data Visualization with ggplot2
	Feb 28th (Friday)	SIGCSE 2025 - NO CLASS
5	Mar 3rd (Monday)
	Mar 5th (Wednesday)
	Mar 7th (Friday)
6	Mar 10th (Monday)
	Mar 12th (Wednesday)
	Mar 14th (Friday)
7	Mar 17th (Monday)
	Mar 19th (Wednesday)
	Mar 21st (Friday)
8	Mar 24th (Monday)	SPRING HOLIDAY - NO CLASS
	Mar 26th (Wednesday)	SPRING HOLIDAY - NO CLASS
	Mar 28th (Friday)	SPRING HOLIDAY - NO CLASS
9	Mar 31st (Monday)
	Apr 2nd (Wednesday)
	Apr 4th (Friday)
10	Apr 7th (Monday)
	Apr 9th (Wednesday)
	Apr 11th (Friday)
11	Apr 14th (Monday)
	Apr 16th (Wednesday)
	Apr 18th (Friday)
12	Apr 21st (Monday)
	Apr 23rd (Wednesday)
	Apr 25th (Friday)
13	Apr 28th (Monday)
	Apr 30th (Wednesday)
	May 2nd (Friday)
14	May 5th (Monday)
	May 7th (Wednesday)
	May 9th (Friday)
15	May 14th (Wednesday)	Final Exam (11:30PM - 2:00PM)