To provide the student with a significant level of comprehension both of the theoretical concepts underpinning databases for Big Data and also how to critically appraise the appropriateness for industry.

1. Defend the selection of an appropriate scripting language for the analysis of data
2. Compose a complex application appropriate to analysis and solution of a large scale problem solving and data analysis requirement
3. Identify, explore and make informed judgement relating to the design, modelling and implementation of data analytics systems paying particular attention to legal and ethical issues
4. Reflect upon their personal professional practice demonstrating appropriate and effective communication and organisational skills in the development of an application as part of a team
5. Analyse complex problems resultant from the use of large-scale data sources for data analysis and defend appropriate approaches to solving the business problem
6. Analyse complex and large-scale big data problems using graphs
7. Apply research, information gathering, critical analysis, design and implementation techniques appropriately and effectively
8. Design and implement standard implementation techniques for the communication of analysis in a business appropriate manner.

This module will provide the student with an in-depth understanding of the theoretical and applied concepts underpinning business intelligence. It will examine the area of dimensional data modelling and the extract transform and load process whereby the student will gain insight and practical skills in building a data warehouse. Business reporting and data visualisation principles and techniques will be explored as a means for creating meaningful reports and visualisations. At the end of this module, the student should be able to collect, process and query data, create interactive visualisations and demonstrate how they provides insight into managerial decision making.

1. Critically discuss business intelligence concepts and its relationship in supporting business decision making.
2. Analyse typical needs of business sectors to select appropriate key performance indicators.
3. Appraise dimensional data modelling techniques to design and build a data warehouse to meet specific user needs.
4. Identify and apply relevant techniques to extract, transform and load data.
5. Examine and develop structures and techniques to prepare and query data for analysis and reporting.
6. Design and create data visualisations based on data visualisation theory to prepare and present effective data visualisations, dashboards and reports for various user needs.

The student will develop key practical skills and a foundation in Machine Learning techniques including data analysis and artificial intelligence and their application on specific problems in Machine Learning, such as, prediction, classification, recommendation and optimization.

1. Critically appraise the performance of a learning system.
2. Summarise and evaluate relevant research into machine learning.
3. Analyse and evaluate the main approaches to machine learning and show similarities and differences between different approaches.
4. Develop and appraise appropriate machine learning classification, optimisation and regression problems.
5. Implement machine learning algorithms to classification, regression and optimisation problems specifically with Big Data.
6. Examine and appraise the ethical and governance considerations associated with machine learning.

To provide the student with a significant level of comprehension both of the theoretical concepts underpinning large scale data architecture for Big Data systems. The student will investigate different data management strategies and have knowledge and skills to design a data management framework. The student will build a data pipeline which incorporates data ingestion, integration, cataloguing and storage techniques using current technologies to create a self-service environment for data users.

1. Discuss data management strategies and roles.
2. Analyse selected data frameworks designs for Big Data architectures.
3. Investigate techniques and technologies available for specific data pipeline stages.
4. Design and implement a data architecture and configuration for a modern data pipeline.
5. Manage and orchestrate a complete data workflow using appropriate tools and techniques.
6. Evaluate best practices for data management systems.

This module will provide the student with a detailed understanding of the steps needed to prepare, statistically analyse, and evaluate data before it is used to build relevant predictive models. Students will examine the procedures needed to pre-process data before analysis begins. They will gain a comprehensive understanding of the steps needed to identify, implement, and examine statistical analysis methodologies and interpret relevant outputs. And the student will gain an understanding of the processes required to implement a predictive model for analysis and interpretation. Students will implement all techniques using a statistical programming language.

1. Explain, analyse, and examine the key components of a statistical programming language to facilitate data science processes.
2. Prepare and implement key Data Science methods and techniques.
3. Critically evaluate the choice of Data Science techniques and tools for a particular scenario with consideration for data and regulatory constraints.
4. Critically evaluate data structure, type, and distribution, and implement relevant hypothesis and statistical tests.
5. Critique and analyse statistical test output using relevant metrics.
6. Prepare data for predictive modelling using preparatory checks and processes required for each model.
7. Critically appraise the outputs from predictive models using evaluation techniques and evaluate their effectiveness to perform as required.

To provide the student with a significant level of comprehension and aptitude for both the theoretical concepts underpinning Big Data analytics using a mathematical approach and critical appraisal of the outputs from Big Data analytics as they relate to business applications.

1. Critically analyse and manipulate large amounts of data.
2. Evaluate statistics derived from large data sets.
3. Understand the nature of statistical inference.
4. Use a statistical package to produce useful visualisations of large data sets.
5. Appraise, review and critically formulate a mathematically-based analysis of large data sets.
6. Determine non-trivial trends/patterns in large data sets.

This module involves working on a research dissertation over an extended period. Working under the guidance of an academic supervisor, the module allows the student to develop theoretical and applied skills in using contemporary computing science techniques both from a theoretical perspective and as an applied discipline. The dissertation represents the capstone work on a topic related to their chosen programme of study.

1. Develop appropriate data collection instruments for mixed methods research and to evaluateeach for their appropriateness to the research question;
2. Engage in a sustained piece of individual, academic research on a chosen topic within the field(s) specifically relevant to the course;
3. Read widely and critically reflect on a number of pieces of written research in an appropriate and thorough manner;
4. Evaluate varying methodological approaches and to adopt the necessary approaches suitableto the topic being researched;
5. Produce a dissertation that evaluates and synthesises quantitative (statistical) approaches such as empirical tests on a hypothesis;
6. Produce a dissertation that evaluates and synthesises qualitative research methods such as the literature review and dissertation proposal and displays evidence of independent research skills.