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Course Description

Introduces the theories and tools for intensive data analysis methods and data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Data analysis software is introduced. Student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.

Program Learning Outcomes (PLOs)

PLO1: Demonstrate the foundational knowledge and skills critical to pursue data analytics as a profession in relation to statistics and math.

PLO2: Articulate and effectively defend the significance and implications of the work in data analytics in terms of challenges and trends in a local, national or global context.

PLO3: Demonstrate the knowledge of advanced tools in data analytics.

PLO5: Apply the principles, tools and methods of analytics to a comprehensive real-world problem or project related to data analyses for tactical and/or strategic decision making.

PLO6: Integrate the major theories, tools, and approaches in data analytics to identify data-driven insights for informed business process management.

SAIL Baseline Mapping

Enter 5 for Central, 4 for Significant, 3 for Moderate, 2 for Minimal, 1 for Potential, or 0 for None					Enter 1 for Passive Engagement, 2 for Active Engagement, or 3 for Generative Engagement
Social Consciousness & Commitment	Global Mindset	Intellectual Agility	Personal & Professional Effectiveness	Well-Being	Level of Engagement
2	2	5	3	1	3

Course Learning Outcomes

Based on satisfactory completion of this course, a student should be able to:

CLO1: Advocate for the use of data mining techniques to the leadership of an organization to achieve stakeholder alignment and business objectives

CLO2: Use the business objectives to discern “sound data” from noise with which to begin modeling to answer the relevant business questions

CLO3: Use data mining, EDA, and storytelling in to address a business problem in a way that conveys actionable insights to your peers and leadership

CLO4: Apply skills and knowledge of the analytics field to promote a data driven culture within an organization

CLO5: Choose, with appropriate reasoning, models and algorithms to complete a data modeling project

Expectations Workload

One (1) academic credit requires 50 minutes a week of classroom or faculty instruction and about two hours of out of class student work for a 15-week course; 100 minutes a week of classroom or direct faculty instruction and about 3.5 hours of out of class student work for a 7.5-week course.
For a three-credit course, students should expect 2.5 hours a week of classroom or faculty instruction and a minimum of 5 hours of out of class student work for a 15-week course; 5 hours of classroom or direct faculty instruction and a minimum of 10 hours of out of class student work for a 7.5-week course.
APA citations

Attendance Policy

As the weekly class session is a vital part of the learning experience, all students are expected to attend every week, be on time for the start of class, and stay until the end of class.

However, in the event of extraordinary, legitimate and unavoidable situations, students may be excused for lateness or absence. Extraordinary, legitimate and unavoidable situations include personal illness, urgent family business, work-related issues, transportation-related issues, religious requirements. If at all possible, students should let the instructor know by e-mail about the excused absence or lateness before class.

案例分类

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