Minimum Grade of C in
STAT 344 or STAT 346 or STAT 250 or MATH 351
IT 206 or CS 112
Week 1: Predicting with probability
Week 3: Data and Statistics
Week 4: Linear regression
Week 5: Model diagnostics
Week 6: Classification
Week 7: In-class mid-term
Week 8: Tree-based methods
Week 10: Lasso and Model Selection
Week 11: Deep Learning
Week 12: Trend forecasting (FPP)
Exam week: Projects due
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 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 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)
You can submit homeworks late with no penalty before they get graded (you are simply taking a chance that your HW won't be graded if submitted past due)
No late submissions for final project accepted.