MSc (Data Science and Analytics)
Overview
NFQ Level 9, Major Award
The MSc in Data Science and Analytics is a full-time programme running for 12 months.
Note: Progression to Part 2 is subject to satisfying the eligibility criteria defined in the programme marks and standards.
Postgraduate Diploma in Data Science and Analytics
Students 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
| Code | Title | Credits |
|---|---|---|
| Students take 90 credits as follows - modules to the value of 60 credits in Part 1 and a Dissertation (30 credits) in Part 2: | ||
| Part 1 | ||
| Students take 60 credits as follows - all listed core modules (30 credits) and 30 credits of elective modules: | ||
| Core Modules 1 | ||
| 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 | |
| Elective Modules 1 | ||
| Students take modules to the value of 10 credits from the following CS modules: | 30 | |
| Programming in Python (10) 3 | ||
| Programming in Python with Data Science Applications (5) 3 | ||
| Complex Systems Development (10) 3 | ||
| Scalable Computing for Data Analytics (5) 3 | ||
| Plus modules to the value of 20 credits, with at least 10 credits of ST (Statistics) modules, from the following: | ||
| Optimisation (5) | ||
| Information Storage and Retrieval (5) | ||
| Topics in Artificial Intelligence (5) | ||
| Data Visualization for Analytics Applications (5) | ||
| Multivariate Methods for Data Analysis (10) | ||
| Operations Research (5) | ||
| Stochastic Decision Science (5) | ||
| Machine Learning and Statistical Analytics I (5) | ||
| Machine Learning and Statistical Analytics II (5) | ||
| Part 2 | ||
| Students take 30 credits as follows: | ||
| Core Modules | ||
| CS6500 | Dissertation in Data Analytics | 30 |
| or ST6090 | Dissertation in Data Analytics | |
| Total Credits | 90 | |
- 1
All selections are subject to approval of the programme coordinator.
- 2
Students who have adequate database experience take CS6408. Students who do not have adequate database experience take CS6503
- 3
Students who have adequate programming experience can take CS6422 and CS6423. Students who do not have adequate programming experience can take CS6506 and CS6507
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 MSc (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; examples include machine learning, data mining and probabilistic methods;
- 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;
- Summarize and communicate, in written and oral form, computational and statistical models and techniques, and be able to visualise this information in order to best present such summaries to technical and non-technical audiences;
- 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;
- Enter graduate or research careers in analytical fields, with the ability to significantly contribute in a broad range of industries (and moreover to society as a whole) in using skills and education to identify, assess, manage and quantify key findings (e.g., trends, risk, uncertainty) in various situations;
- Understand the privacy, legal and ethical issues associated with the storage and analysis of data.