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.
Overview
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
Mr. Anthony Babinec
Anthony Babinec is President of AB Analytics, online data mining instructor at Statistics.com. Specializes in application of statistical and data mining methods in solving business problems.
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
2024
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
2025
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
Prerequisites
Predictive Analytics 1 – Machine Learning Tools
- Skill: Introductory, Intermediate
- Credit Options: ACE, CAP, CEU
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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.
Homework
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.
Software
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 solver.com.
Course Fee & Information
Enrollment
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
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Discounts
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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.
INFORMS-CAP
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.
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