Course Description

This course provides an in-depth study of the field of statistical analysis and data mining as it relates to real-world applications. It explores the complexities of data mining algorithms, software tools, and techniques employed in modern analytics and massive data sets. The selection, application, and evaluation of statistical approaches are examined in the context of data mining.

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 skill set 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 skill set for the company looking for opportunities to stay competitive while maintaining lean and efficient organizations.

Measurable Learning Outcomes

Upon successful completion of this course, the student will be able to:

1. 1. Define the data mining process in relation to Christian principles and ethics. (PLO: 1)
   2. Compare data mining algorithms, tools, and techniques. (PLO: 3)
   3. Analyze data and text-mining case studies. (PLO: 3)
   4. Select appropriate statistical data mining models for various scenarios. (PLO: 2)
   5. Apply data and text mining algorithms to real-world applications. (PLO: 2)

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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 (4)

Discussions are collaborative learning experiences. Therefore, the student is required to create a thread in response to the provided promptly for each discussion. Each thread must be 400–500 words and demonstrate course-related knowledge. In addition to the thread, the student is required to reply to the threads of at least 2 classmates. Each reply must be 200–300 words. Each thread and reply must include at least 2 scholarly sources other than the Learn material and 2 contextually appropriate scriptural references. Each thread and reply must follow the current APA format.

Tutorial Assignments (13 – organized into 8 sets)

The student will complete practical exercises (tutorials) designed to (1) create an experience with the software used in the course, (2) provide real-world examples of problems facing a variety of business sectors, and (3) build an understanding of how to methodically approach solving a data mining hypothesis, and (4) foster a greater understanding of the potential value of corporate data and the impact of big data.