SYST/OR 568. Applied Predictive Analytics - Mason Analytics MS

Instructor: Vadim Sokolov
vsokolov@gmu.edu
Skype: vsokolov83

TA: SeyedOmid HashemiAmiri
ohashem@masonlive.gmu.edu

Course Materials

Starting this week (March 23) this class is moving online. We will use the following online resources:
  1. DataCamp. You need to sign up for it, using the invite link. You will need to use your @masonlive.gmu.edu email to get free access.

  2. Stanford's Statistical Learning class.

  3. ISLR Book

  4. Piazza for discussing HW/project/midterm. You can sign up here

For each week I provide direct links to DataCamp courses and Statistical Learning videos.

Week of March 23

  1. Datacamp: Working with Dates and Times in R

  2. Resampling Methods (ISLR Chapter 5). Videos: a, b, c, d, e, HandsOn

Week of March 30

  1. Midterm due. Please submit 3 files to the BB. (a) First page with your signature acknowledging the honor code (b) PDF file with you solution (do not copy-paste large code chunks, just essential parts that you added on top of my scripts) (c) zip file with R scripts.

Week of April 6

  1. Linear Model Selection and Regularization (ISLR Chapter 6). Videos: a, b, c, d and e, f, g, h, i, j, k, HandsOn

  2. Project proposals Due

Week of April 13

  1. HW3 Due

  2. Tree-based Methods (ISLR Chapter 8). Videos: a, a2, b, c, d, HandsOn

  3. Datacamp: Machine Learning with Tree-Based Models in R

Week of April 20

  1. HW4 Due (ISLR: Ch 8, Ex. 4, 7, 8, 11)

  2. Unsupervised Learning (ISLR Chapter 10). Videos: Unsupervised Learning (Chapter 10) a, b, c, d, e, HandsOn

  3. Datacamp: Unsupervised Learning in R

Week of April 26

  1. Project intermediate reports are Due

  2. Life session discussing the projects

Week of April 27

  1. HW5 Due (ISLR: Ch 10, Ex. 3, 10)

  2. Optimization (I will post my videos on YouTube and will post links here when done)

  3. Datacamp: Communicating with Data in the Tidyverse (Ch 3-4). Plus you can use rmarkdown materials on the official website

Week of May 4

  1. Final Project Presentations Due. Delivered as html file generated from R Markdown. Your HTML pages will be published on the course page and will be publicly available.


Introduces predictive analytics with applications in engineering, business, finance,health care, and social economic areas. Topics include time series and cross-sectional data processing, data visualization, correlation, linear and multiple regressions, classification and clustering, time series decomposition, factor models and causal models, predictive modeling performance analysis, and case study. Provides a foundation of basic theory and methodology with applied examples to analyze large engineering, social, and econometric data for predictive decision making. Hands-on experiments with R will be emphasized.

Syllabus

Place and Time

Grades

Grade composition: Grade based entirely on participation in class, homework assignments, in-class midterm and final project.

Textbooks

Links

Deep Learning

Final Project Data

Very biased and noisy course recommendations

Here are the courses that cover different aspects of data science

Notebooks (ISLR labs)

Previous instances