Predictive Analytics 2 - Neural Nets and Regression

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.  Note:  If you prefer to work in R instead of Excel, there is an R section of this course.

Anticipated learning outcomes:

  • Distinguishing between profiling (explanation) tasks and prediction tasks for linear and logistic regression
  • Specifying and interpreting linear regression models to predict continuous outcomes
  • Specifying and interpreting logistic regression models for classification
  • Using discriminant analysis for classification
  • Using neural nets for prediction and classification
  • Preprocessing text for text mining, and using a predictive model with the resulting matrix

 

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 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
    • Outputlayer
  • 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

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.
Level:
Introductory / Intermediate
Prerequisite:
Organization of the Course:
Options for Credit and Recognition:
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.

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

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 have receive a no-cost license for Analytic Solver Data Mining - this is a special version, for this course.  IMPORTANT:  Do NOT download the free trial version of the software from solver.com (it may conflict with the special course version).

 
Instructor(s):

Dates:

July 12, 2019 to August 09, 2019 November 15, 2019 to December 13, 2019 February 21, 2020 to March 20, 2020 June 26, 2020 to July 24, 2020 October 23, 2020 to November 20, 2020

Predictive Analytics 2 - Neural Nets and Regression

Instructor(s):

Dates:
July 12, 2019 to August 09, 2019 November 15, 2019 to December 13, 2019 February 21, 2020 to March 20, 2020 June 26, 2020 to July 24, 2020 October 23, 2020 to November 20, 2020

Course Fee: $549

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

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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