Click on a module to see its aims and objectives. As illustrated on the course overview page, some are core (mandatory) and some are elective.
Module aims
- Investigate state of the art and research trends in business intelligence and related topics.
- Conduct level 9 research and communicate results.
Module learning outcomes
On successful completion of this module the learner will be able to:
- Evaluate the role and benefits of effective business intelligence in the organisation.
- Demonstrate awareness and critical understanding of developments in data warehouse design and implementation.
- Demonstrate awareness and critical understanding of developments in business intelligence front end tools and techniques.
- Independently research current trends and developments in business intelligence related technologies.
- Apply research methods to their work and differentiate between exploratory, constructive and empirical research.
- Demonstrate awareness and critical understanding of applications in the areas of ETL, Databases, and BI.
- Evaluate and critique current legislation on data privacy and relevant ethical issues.
Indicative module assessment
Module aims
This module is aimed at learners who want to study advanced concepts relating to data science. 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. The module will cover approximately ten algorithms, include algorithms for classification, regression, clustering, assocation analysis and sequence analysis.
Module learning outcomes
On successful completion of this module the learner will be able to:
- Discuss in depth a variety of data mining techniques, and their applicability to various problem domains, including big data analytics.
- Evaluate a business objective and related dataset to assess the appropriateness of a number data mining algorithms in achieving that objective.
- Work through the mining and evaluation stages of a data mining methodology, selecting the most appropriate mining technique, and optimising algorithm parameters to maximise performance.
- Independently research current trends and developments in knowledge discovery related technologies.
- Critically analyse relevant publications to assess the relative merits of methodologies used and conclusions made.
- Self-evaluate work done.
Indicative module assessment
- Literature reviews and analysis of topics relevant to the module.
- Practical assessment to select candidate mining algorithms appropriate to a selection of datasets and mining objectives; compare the results of these mining algorithms. The deliverable is a report justifying, evaluating and analysing the effectiveness of the algorithms used, and the effectiveness of tuning parameter settings on each algorithm. Practical work will be done using Rapidminer an opensource data mining tool.
Module aim
To investigate the properties of data, how to visualise data, and how pre-proposing can improve the information content of data.
Module learning outcomes
On successful completion of this module the learner will be able to:
- Discuss in depth a variety of data preparation techniques, and their applicability to various problem domains
- Research current trends in data visualisation, and select the appropriate graphical representation for data and results
- Understand the links between data and necessary pre-processing algorithms to improve as well as prepare the data for modelling purposes
- Evaluate appropriate techniques to improve data quality, and be aware of the limitations of such techniques
- Analyse a data set to assess what data preparation is required to both clean the data set and expose its information content
- Highlight information content using data visualisation techniques
- Independently research current trends and developments in data preparation techniques
Indicative module assessment
- Literature reviews and analysis covering a range of topics relevant to the module.
- Apply appropriate data exploration and pre-processing techniques on a dataset selected by the student; report on work done detailing decision made with justification. Practical work can be done using Rapidminer and R, both of which are open-source.
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.
Learning outcomes
Having successfully completed this module, the student will be able to:
- Research appropriate data science / data analytics techniques for a specific problem domain.
- Select from, and apply, a range of advanced, state of the art, data analysis, data visualisation and data mining techniques to a practical case study.
- Understand and interpret a business objective, and translate the business objective into data analytics / data science objective(s).
- Identify possible risks and limitations of a data set in achieving a business objective(s).
- Apply the appropriate analysis techniques to match a business objective.
- Present results to stakeholders in terms of the business objective(s) set, and how the information learned can be used to add value to the business.
Indicative module assessment
The module will be evaluated based on a single project running for the duration of the module. The project will be presented as a case study with the initial business objective(s) and data. Students can base their work on an appropriate, work related project, or on a case study provided by the course tutor. The project will follow a recognised methodology such as CRISP-DM, covering all topics on the syllabus. It will be an individual piece of work submitted by each student.
The deliverables will include:
1. An interim presentation on project progress and outstanding issues.
2. Contributions to peer evaluation on the interim report in the form of constructive evaluation, suggestions and advice.
3. A final report to stakeholders on the results on the project with respect to the business objectives, and how the information learned can be used to benefit the business.
4. A publishable paper justifying the methodology used and presenting the results achieved.
Module aims
- Investigate state of the art and research trends in text analytics, including information retrieval and web content mining.
- Critique and evaluate the performance of algorithms for both text analytics and web content mining.
Learning outcomes
Having successfully completed this module, the student will be able to:
- Demonstrate an awareness and critical understanding of ways to extract key concepts and relationships from semistructured and unstructured text, and structure them for data mining.
- Discuss current research activities relating to text analytics and web content mining.
- Understand limitations of current information extraction techniques and the vision for the future.
- Extract key concepts and relationships from semi-structured and unstructured data.
- Apply prediction and clustering techniques to the prepared data, and critically evaluate the results.
- Independently research current trends and developments relating to the processing of semi-structured unstructured
Indicative module assessment
- Literature reviews and analysis covering a range of topics relevant to the module
- Work through all stages of a text analytics project life cycle using an appropriate text mining tool.
- Compile a unique data set by applying web crawling strategies and implementations. Once the data have been obtained, an appropriate analysis technique such as visualisation, classification, association rules or clustering must to be applied.
Module aims
- To introduce the concepts and utility of geographically referenced data and geographic data mining for knowledge discovery in data.
- To explore and critique data analytics techniques and algorithms for mining data with a geographical component.
Module learning outcomes
Having successfully completed this module, the student will be able to:
- Disciss fundamental geographic concepts and principles underlying geograhpic data for GIS.
- Demonstrate awareness and critical understanding of challenges in mining geographically-referenced data in spatial
- database system.
- Apply appropriate (geographic) visualisation tools to the data for analysis.
- Select and apply appropriate exploratory spatial data analysis, data preparation techniques and modelling algorithms to a practical case study.
- Independently research applications, trends and developments in Geographical Data Mining.
Indicative module assessment
Literature reviews and analysis covering a range of topics or applications relevant to the module (40%).
Practical projects and exercises that focus on GIS data, visualisation, data exploration and pre-processing techniques (60%).
Module aim:
Students taking this module will acquire the computer programming skills necessary to analyse and manipulate big data. Big data in this context refers to datasets that are too large to be handled by the software tools commonly used to analyse and manipulate data within a tolerable elapsed time. The algorithms and challenges for processing large datasets form a core part of this course, such that the student will be able to select the appropriate algorithms, tools or methods for big data problems in addition to being able to implement and evaluate solutions using a variety of programming techniques and tools. Students are not expected to have advanced programming skills in order to take the module, but will need to have fundamental knowledge and skills in computer programming.
Module learning outcomes:
Having successfully completed this module the student will be able to
- Clearly describe the characteristics of big data, and contrast the requirements for processing big data with conventional data.
- Identify and illustrate the challenges of programming for big data, and evaluate contrasting methods for addressing these challenges.
- Demonstrate a detailed understanding of the state of the art in Big Data algorithms and techniques.
- Select and evaluate the appropriate development tools for various big data programming problems.
- Demonstrate a detailed understanding of state of the art distributed programming paradigms for both data storage and data analysis, and select the appropriate method for a given context.
- Implement solutions to various big data programming problems using a range of state of the art tools and techniques, and evaluate the effectiveness of these solutions.
- Present an informed view of the changing big data landscape and how programming for big data may change in the future, based on current literature and standards.
Indicative module assessment:
- Weekly exercises in Big Data Programming.
- A major project based assignment where students implement a Big Data solution and execute it on an appropriate platform (e.g. a cloud service, virtual cluster, etc.).
- A technical review(s) in the state of the art of Big Data Analytics.
Module aims
To provide the learner with the statistical concepts and tools necessary for any engineering or science graduate. To do this, the learner will cover the fundamental ideas of probability and descriptive statistics, moving on to Hypothesis testing and the design of experiments.
Learning outcomes
- Summarize large sets of data, including grouped data, using the standard measures of central tendency and dispersion and their definitions and properties, and represent it graphically, by following an agreed set of conventions.
- Apply the laws of probability to questions involving random variables and events, and move on to the concept of a random variable and its distribution, the meaning of expected values, and the properties of common distributions such as the normal, binomial, Poisson and exponential distributions.
- Interpret the concept of a statistic as a random variable arising from sample data, with the central limit theorem determining the behaviour of such statistics and thereby underpinning many statistical tests.
- Frame and use an appropriate test for a statistical problem, based on their knowledge of hypothesis testing, the central limit theorem and those distributions used in a range of common statistical tests. This will include multivariate analyses –Manova, Mancova.
- Design or explain the chosen structure of an experiment and the meaning of any data analysis produced for that experiment, based on the student’s understanding of the properties of Analysis of Variance and Analysis of Covariance and other statistical tests.
- Apply their knowledge of techniques derived from linear algebra to the matrix formulation of the general linear model, including eigenvector decompositions of the covariance matrix and their application to Principal Component Analysis.
Indicative module assessment
- Hypothesis testing I: The student will be given an assignment on Hypothesis testing, implementing a range of the statistical tests covered in the module, including tests on means and variances, tests on group means, correlation and regression, and tests for goodness-of-fit and independence. The student will be assessed on their ability to establish the conceptual framework of any test, the Null and alternative Hypothesis, identify the parameters of a given test and draw the correct conclusions and the meaning of type I and II errors. (20%)
- Hypothesis testing II: The student will be given an assignment on Analysis of Variance, where they will identify a range of experimental designs testing scientific Hypotheses, the corresponding test and the required partitions of sums of squares for the analysis of variance layout. The student will be assessed on their ability to establish the conceptual framework of the tests and drawing the correct conclusions. (25%)
- Probability: The student will be set a number of questions on the theoretical, probability element of the module, including its application to problems such as reliability and quality control, the fundamental definitions of probability, the Central limit theorem and its implications, the properties and definitions of common distributions and the theory of the general linear model. (30%)
- Case study: Interpreting the results of an analysis of an existing or historical data set, writing up a report at an appropriate academic standard on these results, and interpreting them for peers and non-technical colleagues. (25%)
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 analytics and will demonstrate how to apply proven analytics techniques to large multimedia datasets.
Module learning outcomes
Having successfully completed this module, the student will be able to:
- Describe techniques for feature extraction, selection and combination on multimedia data
- Compare and contrast models and algorithms for mining multimedia datasets
- Pre-process or clean multimedia data
- Reduce the dimensionality of multimedia data whilst conserving the relevant information
- Apply proven data science techniques for finding implicit patterns in large multimedia datasets
- Characterise the performance of various mining algorithms on multimedia data
Indicative module assessment
Continuous assessment will include weekly practical exercises, a major assignment and problem sheets based on lecture material.
Students have two options regarding their research project module. You can opt to do:
a) If you have completed six taught modules you will do a one semester, 30-credit research project.
b) If you have completed three taught modules you may consider doing a 60-credit research project, which runs from semester 2 to semester 4.
Both research project modules are describe below.
Module aims
Independent research project to give learners the experience of developing an individual computing project
at postgraduate level. Learners will demonstrate their responsibility for substantial independent working and
a full project from problem specification through to implementation and evaluation.
Module learning outcomes
- Investigate various approaches to research enquiry and develop a research proposal.
- Write a literature review for selected research questions by reporting on relevant existing research demonstrating appropriate academic citation and referencing.
- Demonstrate the purposes and procedures involved in data gathering techniques, data analysis and discussion of results.
- Dissemination of results through presentations and research thesis documentation.
Module aims
This research module builds on your existing postgraduate experience to enable you to complete your
training as a researcher. You’ll develop a research proposal by identifying and explaining a research
problem relevant to your MSc. Your research will involve a literature review, data collection, data analysis,
results and conclusions. You will then communicate the outcome of your research through presentations
and dissertation report.
Module learning outcomes
- Demonstrate self-direction and originality in planning tasks and solving problems during a research project.
- Prepare a comprehensive review or critical evaluation of existing research literature and/or professional guidance on a specific topic.
- Evaluate the research findings in relation to applicable techniques, theoretical limitations and experimental or design considerations.
- Analyse data showing originality in its interpretation in relation to scientific literature.
- Synthesise appropriate conclusions and findings through knowledge and systematic understanding of the research process and any limitations of the work.
- Communicate the outcomes of research to professional standards through a dissertation, poster and oral presentation.