MBAn Software Workshops

Welcome to MIT! Through the Fall and IAP terms, we will host several short workshops intended to build your skills with some fundamental software tools for learning from and solving problems with large data sets. This website hosts the materials for these workshops. The easiest way to obtain these materials is to fork this website in GitHub and then clone the result to your personal computer. You’ll learn what these words mean and how to do them in the preassignment – which you should complete as soon as possible.

In our first two days, we will learn how to perform elementary data analysis in R and solve optimization problems using Julia. We will also learn to use git and GitHub to organize our work, and RMarkdown to easily share the results of data analysis with multiple stakeholders in attractive formats.

Preassignment

You must complete the preassignment prior to the session. In this preassignment, you will install and configure version control tools (git and GitHub); data analysis tools (R and RStudio); and optimization tools (Julia, JuMP, and Gurobi). We will use all of these tools extensively during our time together.

Please note that this first preassignment is especially long: plan to allow 2 hours to complete the entire preassignment, and get started at least two days prior to the beginning of the session. We will not be troubleshooting software installation issues during class time.

Session Materials

  • Introduction: Why Code?
  • Introduction to Data Science in R
  • Introduction to Optimization in Julia

In this week’s workshops, we’ll learn the basics of machine learning in R; work together through a case study that synthesizes our core analytic skills (and adds some new ones); and develop interactive data applications using R and the shiny package.

Preassignment

Prior to the session, you may find it helpful to review your scripts from back in August, as we will be relying heavily on the tools we introduced then, including all our data wrangling and visualization functions.

Additionally, please download and run the preassignment in R to make sure that you have the required software.

Session Materials

  • Statistical Modeling and Machine Learning with R
  • Case Study: Navigating the Cycle of Data Science
  • Interactive Data Products with Shiny

Special topics TBD

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