Spring 2025
> Management & Business
> MIST.6160
> 081
Course No: MIST.6160-081; SIS Class Nbr: 13901; SIS Term: 3430
Course Status: Open
Course Description
The course will cover advanced data mining techniques with applications in different business domains. Students will be introduced to advanced analytic solutions aimed at addressing issues related to big data including volume, variety, and velocity. Topics will focus on performing descriptive and predictive analytics through programmatic analytic platforms as well as text analytics techniques for unstructured or semi-structured data. Concepts will be introduced through a hands-on approach using state-of-the-art analytic platforms and tools.
Prerequisites, Notes & Instructor
- Prerequisites: MIST.6060 Data Mining for bus. Intel., or POMS.6120 Stat. for Predictive Analytics, or POMS.6220 Decision Analytics, or permission of graduate business programs coordinator.
- Special Notes: If not currently matriculated in a Manning School of Business program, please contact the MBA staff at MBA@uml.edu or call 978-934-2848 for permission to take courses.
- Credits: 3; Contact Hours: 3
- Instructor: Prasad Kothapalli
-
UMass Lowell Bookstore
When Offered & Tuition
- Online Course
- 2025 Spring: Jan 21 to Mar 16
- Course Level: Graduate
-
Tuition: $1965
| Pay as little as $400/mo for this course.
Learn more about course payment plans. »
- Note: There is a $30 per semester registration fee for credit courses.
Related Programs: M.S. in Business Analytics, Online MBA
Every effort has been made to ensure the accuracy of the information presented in this catalog. However, the Division of Graduate, Online & Professional Studies reserves the right to implement new rules and regulations and to make changes of any nature to its program, calendar, procedures, standards, degree requirements, academic schedules (including, without limitations, changes in course content and class schedules), locations, tuition and fees. Whenever possible, appropriate notice of such changes will be given before they become effective.