Applied Predictive Analytics

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Applied Predictive Analytics

taught by Ken Strasma

Aim of Course:

In this online course, “Applied Predictive Analytics,” you will apply data mining techniques in a real world case study. The case study concerns microtargeting in political campaigns, but the principles apply equally to any marketing campaign involving individual-level messaging. This course is really a "lab" for practically testing your skills in a real world context.  You should have some facility with R or Python, and some familiarity with predictive modeling, before taking this course.

Note:  This course is also listed as Persuasion Analytics, the study of microtargeting and uplift modeling.  The data in the course are sizeable and complex, and the domain (political targeting) is relatively new and unlikely to be familiar to most students, hence the course is ideal as a real-world case study for analytics students who need to be prepared to apply their analytical skills to new situations.  Students who sign up for Persuasion Analytics will use curated, reduced data sets and an Excel add-in; students who are taking Applied Predictive Analytics as part of their PASS program must use the full dataset and either R or Python.

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

Course Program:

WEEK 1: Setting the scene

  • Why political campaigns need to target
  • Phases of a campaign
  • Finding the right targets for the right phase
  • Getting to know the data
  • Understanding and engineering features
  • Transformations

WEEK 2: Developing Predictive Models

  • Traditional vs. individual level targeting
  • Deciding what to predict
  • The model-building process
  • Assessing models

WEEK 3: Combining Models

  • Ensembles
  • Controlled and Natural Experiments
  • A-B tests
  • Uplift - Combining A-B tests with Predictive Models

WEEK 4: Implementation and Actions

  • Deciding who to target and with what message

 

Homework:

The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.

This course also has example software codes, supplemental readings available online.

Applied Predictive Analytics

Who Should Take This Course:

The course will also be useful as a guided 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.  Statistics.com PASS certificate program candidates (Programming for Data Science) take this course as their capstone project.  

Level:

Introductory / Intermediate

Prerequisite:
You must be familiar with predictive modeling, and have sufficient programming level skills to write predictive model code in R or Python.

Organization: 
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.

Credit
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.

Software:

To do the project in the course you will need access to and some familarity with R or Python.  


Applied Predictive Analytics

Instructor(s):
Dates:
March 03, 2017 to March 31, 2017August 25, 2017 to September 22, 2017March 02, 2018 to March 30, 2018August 24, 2018 to September 21, 2018

Course Fee: $589