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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

Dr. Galit Shmueli

Dr. Galit Shmueli

Dr. Galit Shmueli is a Distinguished Professor of the Institute of Service Science, College of Technology Management at National Tsing Hua University, Taiwan.  Previous academic appointments include the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics and Information Systems at the Indian School of Business, Hyderabad, and Associate Professor of Statistics in the Department of Decision, Operations & Information Technologies at the Smith School of Business, University of Maryland.  Dr. Shmueli’s research has been published in the statistics, information systems, and marketing literature.

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


11/11/2022 to 12/09/2022


03/10/2023 to 04/07/2023
07/07/2023 to 08/04/2023
11/10/2023 to 12/08/2023


03/08/2024 to 04/05/2024


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

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Predictive Analytics 2 – Neural Nets and Regression

Additional Information


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

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 Analytic Solver Data Mining (previously XLMiner) which is available:

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

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

Do NOT download the free trial version of the software from (it may conflict with the special course version).

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)

Register For This Course

Predictive Analytics 2 – Neural Nets and Regression