SYST 468. Applied Predictive Analytics

Department of Systems Engineering and Operations Research
George Mason University
Spring 2019


Minimum Grade of C in

Course Outline


Students will have a in-class midterm exam and take-home 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.


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 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 DataCamp boot camp courses. I will use Anaconda data science platform which was originally designed for Python but now has support for R as well. You can download it here. Use this instructions to set up your environment for R language.


Midterm 30% + Mini-project + 30% + Final exam 30% + Homework 10%. Scores of each component are normalized to be out of 100. Grades will be posted on Bb. Cut-offs: 94 (A+), 90 (A), 87 (A-), 84 (B+), 79 (B), 76 (B-), 74 (C+), 70 (C), 67 (C-), 64 (D+), 57 (D)

Late submissions