SYST/OR 568. Applied Predictive Analytics

Department of Systems Engineering and Operations Research
George Mason University
Spring 2017

Prerequisites

Graduate standing (Undergraduate engineering math: Calculus, probability theory, statistics, and some basic computer programming skills.)

Course Outline

Assignments

Students will have a take-home midterm exam and final project. There are approximately 6 homework assignments; students are encouraged to work in small groups. Each homework has 2-3 ‘‘theoretical questions’’ and 2-3 ‘‘hands-on’’ problems. Theoretical questions will be based on the material covered in class. Hands-on problems will require using R and routines provided by instructor to perform data analysis tasks. For the final project a student or a group of students can choose their own data set and a hypothesis to verify. Instructor will have 1-2 data sets/analysis problems, in case students have hard time identifying it on their own. Work on the final project can begin as soon as class starts. Each group will submit the final report.

Computing

You can choose which software you use. I recommend investing the time to learn R. Python is good choice as well. R is the dominant software package for real world Predictive Analytics and is used throughout other courses. This open-source software is available for free download at www.r-project.org and you can find documentation there.

A great way to start learning is to buy a book and start working through tutorials. A good guide is Adler’s R in a Nutshell. They have many tutorials to help you get up to speed. You can browse other options by searching ‘R statistics’ on Amazon. If you are new to R (and even if not) you should complete a tutorial to familiarize yourself with the language. A great option is the TryR code school.

Grading

Take home Midterm 40% + Final project + 30% + Homework 30%. Scores of each component are normalized to be out of 100. Grades will be posted on Bb. Cut-offs: 97 (A+), 93 (A), 90 (A-), 87 (B+), 82 (B), 79 (B-), 77 (C+), 73 (C), 70 (C-), 67 (D+), 60 (D)

Submission (per Bahman)

Late submissions

Project

There are two options:

The data analysis project will be evaluated based on the following criteria:

Results on the data analysis project should contain:

Final project attribution

Other requirements