Overview
Status: Active
National Framework Of Qualifications (NFQ) Level: 9
NFQ Award Class: Major Award
Total Credits: 60
Connected Curriculum: - Employability
- Inter-and Transdisciplinary
- Research Based Teaching
Graduate Attributes: - Creator, evaluator and communicator of knowledge
- Digitally Fluent
- Independent and creative thinker
Exit Award only
Students on the MSc (Data Science and Analytics) programme who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.
Programme Requirements
For information about modules, module choice, options and credit weightings, please go to Programme Requirements.
Programme Requirements
Module List
| Code |
Title |
Credits |
| |
| CS6405 | Datamining | 5 |
| CS6421 | Deep Learning | 5 |
| ST6030 | Foundations of Statistical Data Analytics | 10 |
| ST6033 | Generalised Linear Modeling Techniques | 5 |
| CS6408 | Database Technology 2 | 5 |
| or CS6503 | Introduction to Relational Databases |
| 30 |
| Programming in Python 3 | |
| Programming in Python with Applications 3 | |
| Complex Systems Development 3 | |
| Scalable Computing for Data Analytics 3 | |
| |
| Optimisation | |
| Information Storage and Retrieval | |
| Topics in Artificial Intelligence | |
| Data Visualization for Analytics Applications | |
| Multivariate Methods for Data Analysis | |
| Operations Research | |
| Stochastic Decision Science | |
| Machine Learning and Statistical Analytics I | |
| Machine Learning and Statistical Analytics II | |
| Total Credits | 60 |
Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards Book and for each module in the Book of Modules.
Programme Learning Outcomes
Programme Learning Outcomes for Postgraduate Diploma in Data Science and Analytics (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:
- Interpret large, heterogeneous data sources by comparing and selecting appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, and probabilistic and statistical methods;
- Describe the fundamental theories, models and principles of statistical methods, and carry out a wide range of calculations involved in statistical decision making, modelling, hypothesis generation and inference;
- Describe the fundamental theories, models and principles of computational methods for storing, processing and performing inference on large data sets;
- Manage large amounts of data using modern database tools, and understand the management implications of hardware, software and bandwidth constraints;
- Analyse data selected from a range of domains such as manufacturing, bio-informatics, marketing, social networking, finance, fraud detection, and drug discovery;
- Perform computational/statistical analyses and create visualizations to aid in understanding heterogeneous data;
- Analyse problems of a computational and/or quantitative nature, encountered in a range of types of large-scale data, and construct solutions to such problems using the tools and skills of modern data analytics, including the use of machine learning, statistical and mathematical computer packages, and the use of database programmes.
