This repository contains the assignments completed as a part of MIT's MOOC 'The Analytics Edge offered on edX.'
MIT's 'The Analytics Edge' is a class that focuses on using statistical tools to gain insights and make predictions using data. The majority of the course teaches analytics methods using the R programming language along with spreadsheet software. The course runs for 11 weeks and covers linear regression, logistic regression, decision trees, text analytics, clustering, data visualization and, linear and integer optimization.
It’s probably one of the best courses out there to learn R in a way that you go beyond the syntax with an objective in mind, i.e., to do analytics and run machine learning algorithms to derive insight from data. I completed this course back in 2015 and have since then repeatedly referred to the archive to maintain a solid foundation in the domain. Since the classroom is not open throughout the year and the assignments are not available in the archived course, I decided to compile my work as presented in this repository (for reference).
This repository contains the homework assignments (available after the end of weekly lectures). They have been organized in directories for every unit involving R Programming. Each directory consists of the dataset(s), R script, RMD file and an HTML export of the same. I suggest you to use the HTML files to view the content; the other files would then help to recreate the work.
- Unit 1 - Introduction to Analytics
- Unit 2 - Linear Regression
- Unit 3 - Logistic Regression
- Unit 4 - Trees
- Unit 5 - Text Analytics
- Unit 6 - Clustering
- Unit 7 - Visualization