| Module Code | STU11004 |
| Module Name | Introduction to Management Science |
| ECTS Weighting[1] | 10 ECTS |
| Semester taught | Semester 1 & 2 |
| Module Coordinator/s | Eoin Delaney (Semester 1) & TBC (Semester 2) |
Module Learning Outcomes
On successful completion of this Module, students will be able to:
Semester 1
- L01: Explain why the value of money decreases as a function of how far in the future it will be available.
- L02: Compute the simple and compound interest of any amount of money, the value of an annuity and the payment of an amortised loan.
- LO3: Assess the performance of predictive systems.
- LO4: Deploy computational solutions in applied predictive problems.
- LO5: Evaluate decision analysis tools used in management sciences.
- L06: Identify and solve problems using different programming strategies (Both Semesters).
- L07: Explain risk averse and risk prone behaviour.
Semester 2 (TBC)
Module Content
This module covers a range of subjects in Management Science at an introductory level. The objectives of the module are to give students an overview of the subject, to teach important basic techniques and introduce systematic thinking about problems. The module will use real-world Management Science problems to illustrate concepts and models.
Teaching and learning Methods
There will be three hours of lectures each week.
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of total | Week set | Week due |
| Examination | In-person Exam (2hrs) | First Semester | 35% | n/a | n/a |
| Coursework | Assignments | First Semester | 15% | TBD | TBD |
| Examination | In-person Exam (2hrs) | Second Semester | 35% | n/a | n/a |
| Coursework | Assignments | Second Semester | 15% | TBD | TBD |
Reassessment Details
Reassessment is an in-person Exam (2hrs).
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 66 hours | |
| Lecture | 66 hours | |
| Laboratory | 0 hours | |
| tutorial or seminar | 0 hours | |
| Other | 0 hours | |
| Independent study (outside scheduled contact hours), broken down by: | 144 hours | |
| preparation for classes and review of material (including preparation for examination, if applicable) | 132 hours | |
| completion of assessments (including examination, if applicable) | 12 hours | |
| Total Hours | 210 hours |
Recommended Reading List
A full reading list will be provided at the start of each semester. No one textbook covers the entire module, but some parts of the following books will be useful.
An Introduction to Management Science: Quantitative Approaches to Decision Making (3rd Edition), David Anderson et al. Cengage. 2017.
Data Science for Business by Foster Provost and Tom Fawcett. 2013.
Introduction to Management Science (10th Edition) by Bernard W. Taylor. Pearson. 2012.
Fundamentals of Machine Learning for Predictive Data Analytics. Algorithms, Worked Examples and Case Studies. John D. Kelleher et al. MIT Press.
Module Pre-requisites
Prerequisite modules: None
Other/alternative non-module prerequisites: None
Module Co-requisites
None