Applied Predictive Analytics
Taught by Mr. Ken Strasma

Applied Predictive Analytics

taught by Ken Strasma

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.   

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

Course Program:

WEEK 1: Dating the Data

  • Common Data Issues
  • Data Modalities
  • Understanding the Data


WEEK 2: Engineering Features

  • Basic Transformations
  • Combining Domain Knowledge with Statistical properties
  • Feature - Model Tradeoff


WEEK 3: Building Models

  • Choosing Modeling Technique
  • Choosing Model Complexity
  • Evaluating and Improving Models


WEEK 4: Sustainable Modeling

  • Monitoring Model Degradations
  • Adapting Models to New Data
  • Active Learning and Semi-Supervised Learning


Applied Predictive Analytics

Who Should Take This Course:

The course will also be useful as a mentored project for anyone who has learned predictive modeling methods using prepared and curated data, and wants to gain experience implementing them in a real-world context with messy data. PASS certificate program candidates (Programming for Data Science) take this course as their capstone project.  


Introductory / Intermediate

Prerequisite(s) are listed for your benefit so you can determine for yourself whether you have the needed background, either from taking 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,  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.




March 04, 2016 to April 01, 2016 August 26, 2016 to September 23, 2016 March 03, 2017 to March 31, 2017 August 25, 2017 to September 22, 2017 March 02, 2018 to March 30, 2018 August 24, 2018 to September 21, 2018

Applied Predictive Analytics


March 04, 2016 to April 01, 2016 August 26, 2016 to September 23, 2016 March 03, 2017 to March 31, 2017 August 25, 2017 to September 22, 2017 March 02, 2018 to March 30, 2018 August 24, 2018 to September 21, 2018

Course Fee: $589

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

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