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Predictive Analytics 2 – Neural Nets and Regression

Predictive Analytics 2 – Neural Nets and Regression

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics to visualize and explore predictive modeling.


In this course 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. The course includes hands-on work with XLMiner, a data-mining add-in for Excel.

Note: We also offer Predictive Analytics 2 using R and Python.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

Upon completing this course students will be able to distinguish between profiling and prediction tasks for linear and logistic regression.  They will be able to specify and interpret linear and logistics regression models, use various analytical tools for prediction and classification, and preprocess text for text mining.

  • Fit linear and logistic regression models
  • Distinguish between prediction tasks and profiling tasks
  • Use discriminant analysis for classification
  • Specify the structure of a neural network
  • Convert text to a form suitable for predictive modeling
  • Use an Excel tool to implement the models in the course

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.

Our Instructors

Course Syllabus

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 and Neural Networks

  • Discriminant analysis for classification
    • Statistical (Mahalanobis) distance
    • Linear classification functions
    • Generating classifications
  • Rare cases and asymmetric costs
    • Integrating class ratios and misclassification costs
  • Neural network structure
    • Input layer
    • Hidden layer
    • Output layer
  • Back propagation and iterative learning

Week 3

Text Mining

  • Representing text in a table
  • Term-document matrix
  • Bag of words
  • Preprocessing of text
    • Tokenization
    • Text reduction
    • Term Frequency – Inverse Document Frequency (TF-IDF)
  • Fitting a predictive model

Week 4

Additional Topics: Looking Ahead

  • Multiclass classification
  • Network analytics
  • Text analytics

Class Dates


03/08/2024 to 04/05/2024
Instructors: Mr. Anthony Babinec
07/12/2024 to 08/09/2024
Instructors: Mr. Anthony Babinec
11/08/2024 to 12/07/2024
Instructors: Mr. Anthony Babinec


03/14/2025 to 04/11/2025
Instructors: Mr. Anthony Babinec
07/11/2025 to 08/08/2025
Instructors: Mr. Anthony Babinec
11/14/2025 to 12/12/2025
Instructors: Mr. Anthony Babinec


Predictive Analytics 1 – Machine Learning Tools

This online course introduces the basic paradigm of predictive modeling: classification and prediction.
  • Skill: Introductory, Intermediate
  • Credit Options: ACE, CAP, CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Predictive Analytics 2 – Neural Nets and Regression

Additional Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.


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

Course Text

f you are using Analytic Solver Data Mining (previously XLMiner)
The required text for this course is Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Analytic Solver Data Mining, 4th Edition (2023), by Galit Shmueli, Peter Bruce, Kuber Deokar, and Nitin Patel. Also available at Amazon here.

If you are using Python
The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, (2019), by Galit Shmueli, Peter Bruce, Peter Gedeck, Inbal Yahav, and Nitin Patel.  Also available at Amazon here.

If you are using R
The required text for this course is Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R, 2nd Edition (2023), by Galit Shmueli, Peter Bruce, Peter Gedeck, Inbal Yahav, and Nitin Patel.  Also available at Amazon here.


This is a hands-on course, and participants will apply data mining algorithms to real data.

This course uses Analytic Solver Data Mining (previously called XLMiner), a data-mining add-in for Excel. We also offer a course using R or Python.

Course participants will receive a license for Analytic Solver Data Mining (previously XLMiner) for nominal cost – this is a special version, for this course.

IMPORTANT:  Do NOT download the free trial version available at

Course Fee & Information

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.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to  Click here for a special introductory discount code.

Invoice or Purchase Order
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.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for college credit.  For recommendation details (level, and number of credits), please see this page. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.


This course is 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.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:






  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)


There is no additional information for this course.

Register For This Course

Predictive Analytics 2 – Neural Nets and Regression