Helzberg School of Management

Data Science and Business Analytics Series

Applied Data Mining

Spring 2014

Description

Data mining and predictive models are at the heart of successful information and product search, automated merchandizing, smart personalization, dynamic pricing, social network analysis, genetics, marketing, national security and many other technology-based solutions to important problems in business.

Examine techniques for predictive and descriptive learning using concepts that bridge gaps among statistics, computer science, and business strategy.

Data mining is a powerful tool used to discover patterns and relationships in data. Learn how to apply data mining principles to the dissection of large complex data sets, including those in very large databases. Explore, analyze and leverage data and turn it into valuable, actionable information for your company.

You Will Learnto

·  Use methods to extract meaning from small and large datasets

·  Develop and use predictive models and analytics to make resources more efficient

·  Understand and use the models for strategic decision-making

Topics Include

·  Decision trees

·  Neural networks

·  Association rules

·  Clustering

·  Case-based methods

·  Data visualization

Course Design

·  This 8-week for-credit course will be taught in a workshop format. Each week a new dataset and method will be used in class to further understand the valuable information contained in the data. Students will bring their laptops and work along with the instructor to get the most from the sessions. The students to gain hands-on experience will complete weekly projects.

Audience

·  Business analysts, both new and experienced in their industry, who wish to leverage their business knowledge alongside analytic techniques as they move from traditional reporting and analysis to more advanced analytics.

·  People trained in computer science that need more experience working with statistical and predictive analytics models.

·  People trained in statistics or other quantitative disciplines that have not worked with large data sets or in an application beyond Excel.

·  People who are motivated to learn the fast growing field of data science.

Prerequisites

·  It is assumed the students have completed a basic statistics course, have some exposure to any programming language, and a desire to find interesting patterns your company’s data.

If a student feels they need a refresher in any of the above, the instructor can recommend several options that can be completed prior the course beginning.

Software

·  The course will be taught using the open-source R software and RStudio. No prior experience in R is required. However the weekly projects can be done in any business analytics software the student might have access to.

Cost and Dates

·  This course will meet on Wednesday nights January 15th to March 8th, 5:45 to 9:00pm in the Helzberg School of Management, Conway Hall, Rockhurst University. The fee for the eight-week course will be $1,260.

For More Information Contact: Valerie Wright 816-501-4823 or