About This Online Course
Continue gaining knowledge and skills in artificial intelligence, machine learning and data science techniques by taking this online training course from statistics.com in predictive analytics, also called predictive modeling, which is the most prevalent form of data mining. This course is the second in statistics.com predictive analytics learning series, and follows the statistics.com course, Predictive Analytics 1 – Machine Learning Tools Using Solver.
After you successfully complete the course you will be able to distinguish between profiling and prediction tasks for linear and logistic regression. You also will know how to specify and interpret linear and logistics regression models, use various analytical tools for prediction and classification and preprocess text for text mining.
This online course is perfect if you are interested in what predictive modeling can provide to your government or business organization, undertake pilots with minimum setup costs, manage predictive modeling projects or work with consultants or technical experts in deployments of these AI models.
The required text for this online training course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 3rd Edition (Wiley, 2016), Shmueli, P., Bruce, P. C., and Patel, N.R. (available on Amazon). Learners must purchase the book before starting the course.
The course uses Analytic Solver Data Mining (previously called XLMiner), a data-mining add-in for Microsoft Excel. Learners will receive a license for Analytic Solver Data Mining for nominal cost—this is a special version of the software specifically for this course.
Important note to learners: do not download the free trial version available at solver.com.
What You Will Learn
- 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 AI models in the course
Anthony Babinec is the president of AB Analytics. For more than two decades, he has specialized in the application of statistical and data mining methods to the solution of business problems. Before forming AB Analytics, Mr. Babinec was Director, Advanced Products Marketing, at SPSS. There he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users.
Mr. Babinec is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he held various officer positions, including president. He received his Master of Arts and Bachelor of Arts in Sociology from the University of Chicago, Illinois.
Who Should Take This Course
This course is designed for marketing and information technology managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.
The statistics.com course, Predictive Analytics 1 – Machine Learning Tools Using Solver, is the prerequisite to this course.
After finishing this course, you can take the statistics.com companion course, Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules, to continue down the learning path in AI, machine learning and data science. This online course teaches the key unsupervised learning techniques of association rules—principal components analysis, and clustering—integrating supervised and unsupervised learning techniques.
A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.
In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.
This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. 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.
$599 (per person)
Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.
Continuing Education Unit Credits
This online course provides 5.0 CEUs upon 50-percent completion.
This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.