Predictive Analytics 2 - Neural Nets and Regression - with R

# taught by Inbal Yahav and Kuber Deokar

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 R, a free software environment for statistical computing.

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 Nets

• 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

• Multiclass classification
• Network analytics
• Project

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 - with R

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:

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:

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. Certificate - 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. CEUs and/or proof of completion - 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.
4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
College credit:
Predictive Analytics 2 - Neural Nets and Regression - with R has been evaluated by the American Council on Education (ACE) and is recommended for the . Note: The decision to accept specific credit recommendations is up to each institution. More info here.

INFORMS CAP:
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 R, by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: "Predictive Analytics 1 - Machine Learning Tools - with R" and "Predictive Analytics 3 - Dimension Reduction, Clustering and Association Rules - with R".

Software:
This is a hands-on course, and participants will apply data mining algorithms to real data.  The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Instructor(s):

Dates:

February 22, 2019 to March 22, 2019 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 - with R

Instructor(s):

Dates:
February 22, 2019 to March 22, 2019 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

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

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