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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Management Science and Business Modelling
Module Code MS002
School School of Electronic Engineering
Module Co-ordinatorSemester 1: Richard Bolger
Semester 2: Richard Bolger
Autumn: Richard Bolger
Module TeachersMary Sharp
Edel Hester
NFQ level 8 Credit Rating 15
Pre-requisite MS001
Co-requisite None
Compatibles None
Incompatibles None
Repeat examination
Array
Description

A module which introduces students to topics in management science and mathematical modelling for business, including machine-learning techniques.

Learning Outcomes

1. Describe different types of mathematical models used to solve common business problems
2. Formulate various problems for solving via modeling and/or machine learning
3. Solve modelling algorithms using tables, graphs and calculation
4. Use software, including machine learning, to model problems and derive possible solutions
5. Interpret the output of software when used to solve business and machine learning problems
6. Recommend strategies via written reports based on the results of mathematical modelling of real world problems



Workload Full-time hours per semester
Type Hours Description
Tutorial20Online tutorials are held in DCU on Saturdays and weekday evenings according to the timetable
Online activity40Interaction with tutor and fellow students
Assignment Completion75Work independently on assessments over the course of the academic year
Assessment Feedback15Assimilate and apply individual and general feedback received on each assignment
Independent Study223Reading course notes and recommended reading. Researching and studying web resources. Library work. Examination preparation.
Directed learning2Examination
Total Workload: 375

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Management Science / Business Modeling
Introduction to: • Linear programming • Graphical methods of solution • The simplex algorithm • Integer programming • Graph theory and application • Project management (CPM & PERT)] • Inventory stock control models • Queuing and waiting line models with applications • Simulation • Decision theory • Markov processes and applications

Machine Learning
• Introduction to Machine Learning • Supervised learning • Unsupervised learning

Assessment Breakdown
Continuous Assessment30% Examination Weight70%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentWritten assignment based on units 1 -510%Week 11
AssignmentWritten assignment based on units 1 - 1110%Week 19
AssignmentWritten assignment based on units 1 - 1410%Week 27
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List

  • David R. Anderson, Dennis J. Sweeney and Thomas A. Williams: 2010, An Introduction to Management Science, International Edition, 13th, South Western College,
  • Eric V. Denardo: 2008, Science of Decision Making: A problem-based approach using Excel: Student edition, John Wiley & Sons,
  • Taylor, B. W.: 2015, Introduction to Management Science, 12th, Pearson,
  • Trevor Hastie and Robert Tibshirani: 2011, The Elements of Statistical Learning: Data Mining, Inference, and Prediction., 2nd, Springer,
  • Gareth James and Daniela Witten: 2016, An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), Springer,
  • Ian H. Witten and Eibe Frank: 2011, Data Mining: Practical Machine Learning Tools and Techniques, 3rd, Morgan Kaufmann,
  • Thom M. Mitchell: 1997, Machine Learning (International edition), McGraw-Hill Education,
  • Negnevitsky, Michael: 2011, Artificial intelligence, Third, Pearson Education Canada,
Other Resources

None
Programme or List of Programmes
ISBSc in Management of Info Tech/Info Syst
ISDIPDiploma in Management of IT/IS
ITBSc in Information Technology
ITDIPDiploma in Information Technology
ITSDiploma/BSc in Information Technology
Date of Last Revision08-DEC-10
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