About This Online Course
Develop greater understanding of artificial intelligence, machine learning and data science tools by discovering the core concepts in predictive analytics—the most prevalent form of data mining—using Solver.
In this online training course from statistics.com, you will be introduced to the fundamental concepts in predictive analytics—the most prevalent form of data mining. The course covers the two core paradigms that account for most public- and private-sector applications of predictive modeling: classification and prediction.
You will learn how to explore and visualize data and 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 and Bayesian classifiers.
You will learn how to combine different models to obtain results that are better than any of the individual models can produce on their own. This online course also covers 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).
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 online training 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: Do not download the free trial version available at solver.com.
What You Will Learn
- Visualize and explore data to better understand relationships among variables
- Organize the predictive modeling task and data flow
- Develop machine learning models with KNN, Naive Bayes and classification and regression tree algorithms using Microsoft Excel tools
- Assess the performance of these machine learning 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:
- Naive Bayes
- Classification and regression trees
- Understand how ensemble models improve predictions
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.
Dr. Galit Shmueli is a distinguished professor, Institute of Service Science, College of Technology Management, at National Tsing Hua University, Taiwan. Her previous academic appointments include the SRITNE chaired professor of data analytics and associate professor, statistics and information systems, at the Indian School of Business, Hyderabad, and associate professor of statistics, Department of Decision, Operations and Information Technologies, Smith School of Business, at the University of Maryland.
Dr. Shmueli’s research has been published in statistics, information systems, and marketing literature. She received her Doctorate and Master of Science in Statistics, Technion from the Israel of Technology and her Bachelor of Arts in Statistics and Psychology from Haifa University, Israel.
Who Should Take This Course
The course is perfect for marketing and information technology managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters and other professionals who wish to understand what predictive modeling might do for their organization, undertake pilots with minimum setup costs and/or manage predictive modeling projects or ongoing predictive modeling deployments.
You should be sufficiently familiar with Python to follow and are well-versed in statistics (or have the equivalent understanding of topics covered in the statistics.com courses: Statistics 1 – Probability and Study Design course and Statistics 2 – Inference and Association.
After finishing this course, you can take these companion courses from statistics.com to continue your learning path in AI, machine learning and data science:
- Predictive Analytics 2 – Neural Nets and Regression – introduces the basic concepts in predictive analytics to visualize and explore predictive modeling.
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules–teaches key unsupervised learning techniques of association rules—principal components analysis and clustering—and includes integration of 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.
$649 (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.