Skip to content
LearnStatisticspowered by UpThink
Sign in / Create account

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
Data Science and Analytics — full setThis course and 10 others in one payment, $250 less than buying them separately. You choose each start date later.$6,900

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:

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

Course dates

2026

  • 11/02/2026to12/02/2026

    Instructors: Mr. Anthony Babinec

  • 12/07/2026to01/04/2027

    Instructors: Mr. Anthony Babinec

  • 10/05/2026to11/05/2026

    Instructors: Mr. Anthony Babinec

    Enrollment closed

Refund policy

Refund: $550 before start, $325 until day 7, none after. Date changes: first one free, then $25 each, until day 7. Full terms

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

Before your course starts, you get back what you paid minus a $100 processing fee. After it starts, until the deadline, which is the end of day 7 after the start date (for some courses, the end of the 10th of the month the course starts instead), you get back 50% of what you paid. After the deadline there is no refund. To ask, use the withdrawal form in your account or email support@learnstatistics.org. A person reviews every request.

Can I transfer to a later start date or withdraw after the course begins?Every course

Yes. You can move to another start date of the same course yourself, from My learning, until its move deadline (by default the end of day 7 after the start date). The first move of each course is free, and each later one costs $25. Withdrawing follows the refund rules above: 50% of what you paid until the deadline, and no refund after it. Moving does not restart the refund deadline: it is based on the earlier of the date you first bought and your new date.

How do I ask for a refund, and how long does it take?Every course

Use the withdrawal form in your account, or email support@learnstatistics.org. A person reviews every request: we reply within 2 business days and confirm the outcome within 5. Approved refunds go back to the card you paid with and usually arrive within 5 to 10 business days. The course then leaves your account and your classroom.

What if the course is cancelled?Every course

If we cancel your session, you choose: a full refund with no processing fee, or a free move to the next available date. The $25 fee for moving dates yourself is never refunded.

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. The instructor for each start date is listed on the course page.

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 in Trenton, New Jersey, which decides what to award for it. The course is taught online, so you take it the same way in New Jersey or anywhere else. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.