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Applied Predictive Analytics



July 03, 2015 to July 31, 2015 January 01, 2016 to January 29, 2016 July 01, 2016 to July 29, 2016 January 06, 2017 to February 03, 2017 June 30, 2017 to July 28, 2017 January 05, 2018 to February 02, 2018 June 29, 2018 to July 27, 2018

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Applied Predictive Analytics

Aim of Course:

In this online course, “Applied Predictive Analytics,” you will apply machine learning techniques in real world case studies. This course is really a "lab" for practically testing your skills on live business projects.  It provides a hands-on mentored case-study approach.   

Business Case Study: We will study data from the "daily deals" industry (consisting of websites like Groupon, Living Social etc. which source local deals to offer each day). The daily deals industry is emerging and highly competitive. The goal will be to predict the revenue from each offering using the given data.

Course Walkthrough: Each week, participants will be given a set of exercises and instructions to work on the raw data or processed data (details below). Users will apply their statistical/machine learning knowledge, along with their business understanding, to solve the problems and interpret the modeling results for the given business objective. The course will follow an iterative approach for problem solving, in which users are required to submit modeling responses multiple times.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: Business Case Study, Introduction and Data Pre-processing

  • Course and business case study introduction
  • Data Cleaning, pre-processing & data visualization
  • Understanding business objective to be modelled based on the case study

Assignment: To read data in R and perform simple statistical measures. Perform data cleaning, data pre-processing and data manipulation as required by the case study problem.

WEEK 2: Classification Problem, Data Sampling, and Feature Selection

  • Introduction to machine learning
    • Supervised classification problem (Machine learning packages in R)
  • Perform feature selection and processing on given data
  • Model building based on classification techniques
Assignment: To perform simple exercises for machine learning packages in R. Converting raw data into features or creating new features, if needed for the modelling objective.

WEEK 3: Modeling & Evaluation, Leaderboard Prediction

  • Build machine learning model like decision trees, ensemble modelling etc.
  • Evaluation metric like confusion matrix, RMSE etc.

Assignment: Perform basic to intermediate level machine learning model for the problem. These scores will be reflected on the private contest leaderboard for all participants.

WEEK 4: Visualization and Analysis & model interpretation for business case study

  • Model building and visualize the output of the model
  • Interpret modelling results for case study objective

Assignment: Final week assignment is based on understanding the various model outputs for the business objective.

Applied Predictive Analytics


July 03, 2015 to July 31, 2015 January 01, 2016 to January 29, 2016 July 01, 2016 to July 29, 2016 January 06, 2017 to February 03, 2017 June 30, 2017 to July 28, 2017 January 05, 2018 to February 02, 2018 June 29, 2018 to July 27, 2018

Course Fee: $629

Do you meet course prerequisites? What about book & software? (Click here to learn more)

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Applied Predictive Analytics

Who Should Take This Course:

Business analysts, R users, SAS users, statistical analysts who want to learn to implement and apply machine learning techniques to solve predictive analytics problems in a real world business case. Data mining analysts who want extend their knowledge to include machine learning techniques for data modelling.


Introductory / Intermediate

These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

You must have basic programming level skills to write R code and to be able to optimize and experiment with code.  Provided you are adequately familiar with R, our courses Predictive Analytics 1 and 2, or equivalent experience of your own, may be substituted for the "Data Mining in R" requirement. 


This course takes place online, at the Institute’s learning management system for 4 weeks. During each week, you participate at times of your own choosing - there are no set times when you must be online. Participants are given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor throughout the entire period.


The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to study the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of each week,you will receive individual feedback on your assignment.

Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

Depending on topic being covered, reference materials will be provided as required.


You must download and install a copy of R for the course and also R-Studio – an open source IDE for R.

After installing R & R-Studio in your computer you must also install several R add-on packages. Instructions for this installation will be provided as needed. You can install packages directly from R-Studio, instructions will also be given during the course.

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