PA2Undergraduate courses
Predictive Analytics II — Neural Nets and Regression
Linear and logistic regression, discriminant analysis, neural networks and text mining — the second layer of predictive modeling techniques.
- Intermediate
- 4 weeks
- Approx. 15 hours per week
- 3 semester hours
About this course
This course expands your predictive modeling toolkit. You will start with linear and logistic regression used for two distinct purposes: describing relationships between variables and predicting or classifying new records—two tasks governed by different rules for variable selection. Next, you will explore discriminant analysis, rare-event handling, and asymmetric misclassification costs. You will then examine neural network architectures and backpropagation, and learn how to transform unstructured text into structured data ready for modeling. The final week introduces multiclass classification, network analytics, and text analytics.
Who this course is for
Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists and forecasters. It is especially useful for anyone who wants to understand what predictive modeling is worth to an organization, undertake a pilot project, manage a modeling initiative, or work alongside the technical experts who build the models.
What you will learn
7 outcomes
By the end of this course, you will be able to:
- Fit linear and logistic regression models
- Distinguish between prediction tasks and profiling tasks
- Use discriminant analysis for classification
- Integrate class ratios and misclassification costs when cases are rare
- Specify the structure of a neural network
- Convert text to a form suitable for predictive modeling
- Use software tools to implement the models in the course
Week by week
4-Week curriculum overview
This curriculum is identical across all start dates. Expand a week to explore the topics covered.
- Weeks
- 4
- Your week
- Approx. 15 hours
- Level
- Intermediate
Linear and logistic regressionWeek 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
Discriminant analysis and neural networksWeek 2
- 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
Text miningWeek 3
- 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
Additional topics — looking aheadWeek 4
- Multiclass classification
- Network analytics
- Text analytics
Instructors
Expert-Led Guidance
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Mr. Anthony Babinec
BA in Sociology and MA in Sociology, with a focus on advanced statistics and political sociology, from the University of Chicago. He serves on the editorial board of the Journal of Targeting, Measurement and Analysis for Marketing, and has presented at the AMA's Applied Research Methods Conference, the Advanced Research Techniques Forum, the Sawtooth Software Conference and Statistical Innovation's Statistical Modeling Week.
Before you start
What you need to know first
Predictive Analytics I — Machine Learning Tools
Course Format & Schedule
This is a 4-week, 100% online, asynchronous course.
- No mandatory live sessions: Log in and complete your work at times that fit your schedule.
- Weekly Releases: At the start of each week, you will receive new lecture materials and answer keys for the previous week's exercises.
- Interactive Community: Work through exercises, submit assignments, and engage with your instructor and peers via a private discussion board.
Homework
Assigned readings, short-answer questions and guided data analysis problems using software, alongside supplemental video lectures. There is an end-of-course data modeling project.
Texts
Please choose one of the following textbooks based on the software you plan to use:
- Python: Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python (2nd ed., 2025) by Shmueli, Bruce, Gedeck, and Patel. Also available at Amazon here.
- R: Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R (2nd ed., 2023) by Shmueli, Bruce, Gedeck, Yahav, and Patel. Also available at Amazon here.
- Analytic Solver Data Mining (previously XLMiner): Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Analytic Solver Data Mining (4th ed., 2023) by Shmueli, Bruce, Deokar, and Patel. Also available at Amazon here.
The same text is also used in Predictive Analytics I — Machine Learning Tools and Predictive Analytics III — Dimension Reduction, Clustering and Association Rules. So one copy covers all three courses.
Software
This is a hands-on course in which you will apply data mining algorithms to real datasets. The course can be completed using Python or R, both of which are free, open-source programming languages. Corresponding editions of the course text are available for Python and R, making these the recommended options for completing the course without additional software costs. Worked examples are also available in Analytic Solver Data Mining (ASDM), an add-in for Microsoft Excel. If you choose this option, you will need both Microsoft Excel and ASDM. Course participants will receive a license for ASDM for nominal cost — this is a special version for this course. IMPORTANT: Do NOT download the free trial version available at solver.com
FAQ
I have used regression before. Is week one worth my time?This course
Yes, because it is regression put to a different use. Most regression training is descriptive — which coefficients are significant, what the model says about the relationship. Here the same model is judged on records it has not seen, which changes how predictors are chosen and how the fit is assessed. Week one covers both readings side by side so the difference is explicit.
Do I need a mathematics background for the neural network week?This course
No. The network is taught structurally — what the input, hidden and output layers hold, and how back propagation adjusts weights over repeated passes — and you fit and assess one in software rather than deriving the gradients by hand.
Is there a money-back or satisfaction guarantee?Every course
We handle cancellation and refund requests on an individual basis. If a course is not meeting your expectations or your circumstances change, please reach out to our team (support@learnstatistics.org) so we can work with you on a solution.
Can I transfer to a later start date or withdraw after the course begins?Every course
Yes. If you need to defer your enrollment to a future start date, withdraw mid-course, or transfer your seat to a colleague, please contact us directly. We address these requests flexibly on a case-by-case basis.
Who teaches the course?Every course
Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.
Something not answered here? Ask us before you book — and the terms set out what a purchase covers.
Credit, and where it counts
This course carries a verified figure of 3 semester hours. A credit recommendation from the American Council on Education says what a course is worth; the institution receiving it decides whether to award it.
Anyone can take this subject: there is no application, and you do not have to be studying at a university. It also counts toward a degree at Thomas Edison State University, which decides what to award for it. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.