Statistical Analysis and Data Mining – CSIS 657
CG • Section 8WK • 11/08/2019 to 04/16/2020 • Modified 02/01/2024
Course Description
For information regarding prerequisites for this course, please refer to the Academic Course Catalog.
Rationale
Technology’s role in society continues to expand in application and influence. The data generated through this digital frontier is growing exponentially, creating new challenges as well as exciting opportunities. The ability to sift through the vast amount of information requires a skillset that is part engineer and part artist. Finding data, making appropriate associations between data, constructing ways to communicate relationships of data, and applying business intelligence and analytics are fundamental to business in the digital age. Never before has there been so much information available for companies to strategize with, analyze, consume, and even market. The effective exploitation of data mining and predictive analytics technologies will be an invaluable skillset for the company looking for opportunities to stay competitive while maintaining lean and efficient organizations.
Course Assignment
Course Requirements Checklist
After reading the Course Syllabus and Student Expectations, the student will complete the related checklist found in the Course Overview.
Discussions (2)
Discussions are collaborative learning experiences. Therefore, the student is required to create a thread in response to the provided prompt for each discussion. This thread must be at least 400 words and demonstrate course-related knowledge. This thread must be supported by 1 scholarly source and 1 contextually appropriate scriptural reference. In addition to the thread, the student is required to reply to the threads of at least 2 classmates. Each reply must be at least 200 words. Each thread and reply must follow current APA format.
Project Assignments (8)
The student will complete practical exercises (projects) designed to (1) create experience with the software used in the course, (2) provide real-world examples of problems facing a variety of business sectors, (3) build understanding of how to methodically approach solving a data mining hypothesis, and (4) foster a greater understanding of the potential value of corporate data.
