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
Dr. Galit Shmueli
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
2022
Instructors:
2023
Instructors:
Instructors:
Instructors:
2024
Instructors:
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
Homework
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.
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).
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:
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- 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)
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Predictive Analytics 2 – Neural Nets and Regression