STU11004 – Introduction to Management Science
Source: https://teaching.scss.tcd.ie/module/stu11004-introduction-to-management-science/ Parent: https://teaching.scss.tcd.ie/management-science-and-information-systems-studies/msiss-year-1/
| 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) & Soumyabrata Dev (Semester 2) |
| Academic Year | 2026-2027 |
Module Learning Outcomes
On successful completion of this Module, students will be able to:
Semester 1
- LO1: Deploy computational solutions in applied predictive science problems.
- LO2: Critically assess the performance of predictive systems.
- LO3: Evaluate decision analysis tools used in management sciences (e.g., decision trees).
- L04: Apply the time value of money principles to solve problems involving interest, annuities, and amortised loans.
- L05: Identify and solve problems using different programming strategies (both semesters).
- L06: Explain risk averse and risk prone behaviour.
Semester 2
- LO1: Connect digital business models with quantitative decision making
- LO2: Use optimisation and decision analysis to structure managerial choices
- LO3: Study operations and inventory to understand cost and service tradeoffs
- LO4: Apply forecasting to predict future outcomes and support planning
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 and data driven techniques to illustrate concepts and models.
Teaching and learning Methods
There will be three hours of classes 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 | In-class test | 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 | |
| Laboratory | |
| tutorial or seminar | |
| Other | |
| Independent study (outside scheduled contact hours), broken down by: | 144 hours |
| preparation for classes and review of material (including preparation for examination, if applicable) | |
| completion of assessments (including examination, if applicable) | |
| 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