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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.

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

$699 | Enroll Now
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  • Overview
  • Learning Outcomes
  • Instructors
  • Syllabus
  • Dates
  • Prerequisites
  • Student Stories
  • FAQS
  • Requirements
Menu
  • Overview
  • Learning Outcomes
  • Instructors
  • Syllabus
  • Dates
  • Prerequisites
  • Student Stories
  • FAQS
  • Requirements

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 Level
4-Week Course
100% Online Courses
ACE + CAP Credit Eligible
Expert Instructors
Teacher Assistant Support
Tution-Back Guarantee

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.

Instructors

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Dr. Anthony Babinec

Mr. Anthony Babinec

Anthony Babinec is the President of AB Analytics. For over two decades, Tony Babinec has specialized in the application of statistical and data mining methods to the solution of business problems. Before forming AB Analytics, Babinec was Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President.

See Instructor Bio
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.

See Instructor Bio

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

2023

Jul 7, 2023 to Aug 4, 2023

Nov 10, 2023 to Dec 8, 2023

2024

Mar 8, 2024 to Apr 5, 2024

2025

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

The courses listed below are prerequisites for enrollment in this course:

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Predictive Analytics 1 – Machine Learning Tools

Predictive Analytics 1 – Machine Learning Tools

This online course introduces the basic paradigm of predictive modeling: classification and prediction.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024, May 10, 2024

What Our Students Say​

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Left Square Qoute

The videos are excellent! The information is presented in a very clear and effective way.

Ciprian Alexandru Naghiu
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Left Square Qoute

Thank you so much Professors for this useful course. I very much liked XLMiner, it is indeed a very simple yet powerful tool

Nikhil Gupta
VP, Market Data Engineering at Goldman Sachs
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Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

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Predictive Analytics 2 – Neural Nets and Regression with Python

Predictive Analytics 2 with Python – Neural Nets and Regression

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, SQL, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jul 7, 2023, Nov 10, 2023, Mar 8, 2024
Predictive Analytics 2 – Neural Nets and Regression with R

Predictive Analytics 2 with R – Neural Nets and Regression

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jul 7, 2023, Nov 10, 2023, Mar 8, 2024
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules

Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules

This course will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python

Predictive Analytics 3 with Python – Dimension Reduction, Clustering, and Association Rules

This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using Python | Skill: Introductory, Intermediate | Credit Options: CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024
R Programming - Intermediate

Predictive Analytics 3 with R – Dimension Reduction, Clustering, and Association Rules

This course, with a focus on R, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024

Additional Course 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

The homework in this course consists of short answer questions to test concepts, guided exercises in writing code, and guided data analysis problems using software.

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.

Please order a copy of your course textbook prior to course start 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 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 of the software from solver.com (it may conflict with the special course version).

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

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
Contact us to get information on group rates.

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

New to Statistics.com?  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.

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.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At Statistics.com, 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:

 

Chrome

  •  Color Enhancer (for colorblindness)
  • HelperBird (for colorblindness, dyslexia, and reading difficulties)

 

Firefox

  • Mobile Dyslexic
  • Color Vision Simulation (native accessibility feature)
  • Other native accessibility features instructions

 

Safari

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

Miscellaneous

There is no additional information for this course.

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Predictive Analytics 2 – Neural Nets and Regression
$699 | Enroll Now
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Statistics.com offers academic and professional education in statistics, analytics, and data science at beginner, intermediate, and advanced levels of instruction. Statistics.com is a part of Elder Research, a data science consultancy with 25 years of experience in data analytics.

 The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV)

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