Learn to turn raw data into insights using R and build your foundation in modern data analysis.
Data analysis is the process of converting data into valuable information to inform decision-making. This course provides a foundation in the tools, techniques, and common practices used in the industry. It covers the full lifecycle of a data analysis project, including how to obtain, manipulate, explore, model, and present data.
We will explore different analytical approaches and frameworks, using popular tools like R and Python. The course emphasizes hands-on application, with R being the primary language for instruction and examples. You will learn to prepare raw data for use, perform exploratory analysis, and apply techniques like regression, simulation, and forecasting. We will also cover various graphing and visualization tools to help you understand and present your findings.
Additionally, the course now includes an introduction to leveraging Generative AI for data analysis. You will use an AI-based tool to generate and validate R programs, helping you streamline your workflow.
By the end of the course, you will be able to apply a working framework to any data analysis project and use R or Python to complete a large-scale project, including a professional write-up with insights and visualizations. All tools are open-source, except for a trial version of the AI tool.
Topics Include:
Some programming experience is recommended. (R will be covered in class and used in examples. Python experience can be helpful.) Basic knowledge of probability and statistics required, at the level of basic statistics textbooks (see example: ).
This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.
To see all meeting dates, click "Full Schedule" below.
You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.
Recommended Texts:
Data Analysis with Open Source Tools, Philipp J. Janert, O'Reilly Media, 2010. ISBN-10: 0596802358, ISBN-13: 978-0596802356.
R Cookbook, Paul Teetor, O'Reilly Media, 2011. ISBN-10: 0596809158, ISBN-13: 978-0596809157.
The Art of R Programming: A Tour of Statistical Software Design, Norman Matloff, No Starch Press, 2011. ISBN-10: 1593273843, ISBN-13: 978-1593273842.
R in a Nutshell: A Desktop Quick Reference, Joseph Adler, O' Reilly Media, 2012. ISBN-13: 978-1449312084 ISBN-10: 144931208X
