Predictive Analytics 2

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Predictive Analytics 2- Neural Nets and Regression

taught by Anthony Babinec and Galit Shmueli

Aim of Course:

In this online course, “Predictive Analytics 2 - Neural Nets and Regression,” you will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis and neural networks. The course includes hands-on work with XLMiner, a data-mining add-in for Excel.

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

Course Program:

WEEK 1: Linear and Logistic Regression

  • Review Predictive Analytics 1
  • Linear regression for descriptive modeling
    • Fitting the model
    • Assessing the fit
    • Inference
  • Linear regression for predictive modeling
    • Choosing predictor variables
    • Generating predictions
    • Assessing predictive performance
  • Logistic regression for descriptive modeling
    • Odds and logit
    • Fitting the model
    • Interpreting output
  • Logistic regression for classification
    • Choosing predictor variables
    • Generating classifications and probabilities
    • Assessing classification performance

WEEK 2: Discriminant Analysis

  • Discriminant analysis for classification
    • Statistical (Mahalanobis) distance
    • Linear classification functions
    • Generating classifications
  • Rare cases and asymmetric costs
    • Integrating class ratios and misclassification costs

WEEK 3 - Neural Nets

  • Neural network structure
    • Input layer
    • Hidden layer
    • Outputlayer
  • Back propagation and iterative learning

Week 4 - Additional Topics: Looking Ahead

  • Multiclass classification
  • Network analytics
  • Text analytics

In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.

Predictive Analytics 2 - Neural Nets and Regression

Who Should Take This Course:

Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.  This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.


Introductory / Intermediate

Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be 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.

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 go over 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 the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

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.
Predictive Analytics 2 - Neural Nets and Regression has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree category, 3 semester hours in business analytics, predictive analytics, or data mining. Note: The decision to accept specific credit recommendations is up to each institution. More info here.
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP®analysts accrue Professional Development Units to maintain their certification .

Course Text:

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 3rd Edition, by Shmueli, Patel and Bruce.



This is a hands-on course, and participants will apply data mining algorithms to real data.  The course is built around XLMiner, which is available:

  • For Windows versions of Excel, or
  • Over the web

Course participants will have access to a no-cost license for XLMiner.

Predictive Analytics 2 - Neural Nets and Regression

February 24, 2017 to March 24, 2017June 30, 2017 to July 28, 2017October 27, 2017 to November 24, 2017February 23, 2018 to March 23, 2018June 29, 2018 to July 27, 2018October 26, 2018 to November 23, 2018

Course Fee: $549