Master of Science in Computing (Business Intelligence & Data Mining)
|Course code: BN518||
Entry Route into programme:
|Duration: 2 years (4 Semesters)|
|NFQ level: 9|
|Fee: €2,000 per year|
|Schedule: Online Year 1 : Tuesday and Wednesday 6pm - 10pm ; Online Year 2: Wednesday and Thursday 6pm - 10pm. All classes are recorded|
|Award title: Master of Science|
|Credits for Full Award: 90 @ NFQ Level 9|
|Awarding Body: ITB|
Have a listen to what this course is all about.
See www.dataminingmasters.com for more info about this course.
Minimum Entry Requirements
The minimum entry requirement
for this stream is a
Second Class Honours Grade 2 (GPA 2.5 or equivalent) in an NFQ Level 8 Degree in Computing, Science, Engineering, Business with IT, or equivalent.
Applicants not meeting this entry requirement may be admitted to the programme on the basis of extensive practical and/or professional experience which can be assessed by the Institute's APL/APEL process.
This course is designed to produce graduates with the knowledge and skills to:
- Select, apply and evaluate business intelligence and data mining techniques which are focused on discovering knowledge that can be acted on to add value to a company.
- Bring both an in-depth theoretical understanding, and the practical hands-on experience, to a data exploration and mining project including implementing novel and emerging techniques.
- Keep abreast of current research and business intelligence related topics.
Employment Potential for MSc in Computing Graduates
Because we work very closely with local industry, we are aware of the strong need for graduates with specialist information technology skills able to cope with an ever expanding IT environment. The specific skills required now by the IT industry include computing, software development, scalable back-end operating systems and server management, distributed computing, web development and web services, enterprise computing with the management of the enterprise information resource, localisation and language skills in software, information security and digital forensics.
The course will be offered over four semesters (2 years).
All lectures and class discussions will take place in an online class room environment. Classes are also recorded and made available online. Modules are assessed through continuous assessment only, and do not have an end of term exam. Continuous assessment will include theoretical work in the form of literary reviews, and practical work using open source software tools.
|Year 1 Semester 1|
|Year 1 Semester 2|
|Year 2 Semester 3|
Students complete two of the three elective modules. Modules will be offered based on student demand and lecturer availability.
|Year 2 Semester 4|
Students must complete 6 modules and a research project. All Modules are 10 ECTS Credits. The Research Project is 30 ECTS Credits.
A choice of modules across streams
will be offered over an academic year.
Electives will only be available at the discretion of the institute, subject to availability of lecturing staff and sufficient expression of interest from students.
Course Modules :
Business Intelligence Module Aims :
Investigate state of the art and research trends in business intelligence and related topics including how business intelligence adds value; BI architecture, BI front-end, Data privacy and ethics; Business Intelligence and Data Mining methodologies, and the Business Intelligence life cycle.
Data Mining Algorithms Module Aims :
This module is aimed at students who want to study advanced concepts relating to knowledge discovery in data. Using both lectures and independent research, the module will address a number of issues relating to understanding and optimising the performance of data mining algorithms.
Data Pre-processing and Exploration Module Aims :
To investigate the properties of data, how to visualise data, and how pre-proposing can improve the information content of data.
Business Intelligence and Data Mining Applications Module Aims :
Apply state of the art business intelligence, data preparation and data mining techniques to a specific case study and dataset.
Starting with a business objective and data, work through all stages of an appropriate methodology to extract knowledge from the data in accordance with the business objectives, and present the results to stakeholders in the appropriate language, highlighting how the knowledge learned can be used to add value to the business.
Text Mining & Web Content Mining Module Aims :
Investigate state of the art and research trends in text mining, including information retrieval and web content mining.
Critique and evaluate the performance of algorithms for both text mining and information retrieval
Multimedia Mining Module Aims :
Traditional data mining has proved to be a successful approach to extracting new knowledge from collections of structured digital data usually stored in databases. Whereas data mining was done in the early days primarily on numerical data, the tools needed today are tools for discovering relationships between objects or segments within multimedia document components, such as classifying images based on their content, extracting patterns in sound, categorising speech and music, and recognising and tracking objects in video streams. This module will introduce the fundamental concepts of multimedia data mining and will demonstrate how to apply proven mining techniques to large multimedia datasets.
MSc Research Project :
To give students the experience of an individual computing project at postgraduate level.
To give students responsibility for substantial independent working and a full project life cycle, from problem specification through to implementation and evaluation
How to Apply ?
For further details please contact the marketing team on 01 885 1530 or e-mail: email@example.com.