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Curricular information is subject to change
On completion of this module, students should be able to:
1) identify the main classes of analytics problems arising in business and industry;
2) formulate, explain, and distinguish between various forms of regression, classification, and linear programming models;
3) create and implement regression, classification and linear programming models (in suitable software);
4) test the models and interpret results in a form suitable for a business client or manager.
Introduction to Business Analytics;
Data Management;
Linear Regression;
Classification;
Linear Programming.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Small Group | 12 |
Specified Learning Activities | 36 |
Autonomous Student Learning | 40 |
Total | 112 |
MIS20010
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: End of semester individual assignment | Coursework (End of Trimester) | n/a | Graded | No | 70 |
Continuous Assessment: Assignments/Mini-Projects/MCQ | Throughout the Trimester | n/a | Graded | No | 30 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities
Automated feedback on MCQ; Team feedback (Grade plus comment) pre team project, plus general feedback to the class; Solutions to self-assessment exercises on VLE.