Predictive Analytics 1 – Machine Learning Tools
This online course introduces the basic paradigm of predictive modeling: classification and prediction.
Overview
In this online course, you will be introduced to the basic concepts in predictive analytics, the most prevalent form of data mining. This online course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. This course is useful for marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters and professionals who want to understand what predictive modeling might do for their organization, undertake pilots with minimum setup costs, or manage predictive modeling projects or ongoing predictive modeling deployments.
This course uses Analytic Solver Data Mining (previously called XLMiner), a data-mining add-in for Excel. We also offer this course using R or Python.
- Introductory, Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
Students will learn how to explore and visualize the data, how to get a preliminary idea of what variables are important, and how they relate to one another. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. Then you will learn how to combine different models to obtain results that are better than any of the individual models produce on their own. This online course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model).
- Visualize and explore data to better understand relationships among variables
- Organize the predictive modeling task and data flow
- Develop machine learning models with the KNN, Naive Bayes and CART algorithms using Excel tools
- Assess the performance of these models with holdout data
- Apply predictive models to generate predictions for new data
- Partition data to provide an assessment basis for predictive models
- Choose and implement appropriate performance measures for predictive models
- Specify and implement models with the following algorithms:
- k-nearest-neighbor
- Naive Bayes
- Classification and Regression Trees
- Understand how ensemble models improve predictions
Who Should Take This Course
Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This online 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
Preparation
- What is supervised learning
- Data partitioning and holdout samples
- Choosing variables (features)
- Handling missing data
- Visualization and exploration
Week 2
Classification and Prediction
- Assessing classification models
- Confusion matrix
- Misclassification costs
- Lift
- Assessing prediction modelsCommon metrics
- Common metrics
- K-Nearest-Neighbors (KNN)
- Measuring distance
- Choosing k
- Generating classifications and predictions
Week 3
Bayesian Classifiers and CART
- Full Bayes classifier
- Naive Bayes classifier
- Classification and Regression Trees (CART)
- Growing the tree
- Avoiding overfit – pruning
- Using trees for classifications and predictions
Week 4
Ensembles
- Combine multiple algorithms
- Improve results
Class Dates
2025
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
Instructors: Mr. Anthony Babinec
Prerequisites
You will benefit from some familiarity with regression, which is covered in Statistics 2 – Inference and Association.
Introductory Statistics for Credit
- Skill: Introductory, Intermediate
- Credit Options: ACE, CAP, CEU
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Predictive Analytics 1 – Machine Learning Tools
Additional Information
Time Requirements
This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.
Homework
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project. Note: There will be a mid-week discussion exercise in the first week of the course.
In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.
Course Text
If 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
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
ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 3 semester hours in predictive analytics, data mining, or business analytics. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.
Supplemental Information
Take a 10-question quiz on analytics: Test Yourself
Watch our preview of this course:
Watch this video by Dr. Shmueli on “Data Mining in a Nutshell”.