Data Science MSc
2 Years Full-Time (with Advanced Practice) | September Start

In this module you will learn about research and the processes involved in carrying out research in the area of computer and information sciences. The module will encompass the full research cycle from development of the initial concept through to final evaluation and reflection on your research. You will learn how to search, find and evaluate the literature and how to use this to construct a critical literature review. You will also be able to gain an understanding of different research methods and techniques and how to apply them in practice. You will be given the opportunity to gain expertise in data and information analysis, from data collection through to the analysis, presentation and interpretation of results. You will be able to critically evaluate and reflect on the research process. You will also learn about ethics and professional conduct whilst undertaking research and consider and manage risk (including health and safety) and legal, societal and sustainability issues with respect to a research project.

Alongside this you will be provided with the skills and techniques to effectively manage a project from start to finish, including the planning and monitoring aspects.

In this module you will develop knowledge and skills that will enable you to tackle a realistic big data problem, using some of the principal machine learning techniques and statistical approaches used in big data analysis. Furthermore, you will learn how to implement your solution using an industry leading Cloud computing provider together with appropriate distributed processing environments.

You will learn how to host multi-terabyte sized big datasets using a cloud service provider. This will includes provisioning a commercial cloud provider, and then mastering appropriate distributed operating systems, such as Hadoop. You will then learn approaches to processing and analysing big data, based on advanced statistical processing, supervised and unsupervised machine learning algorithms and other state of the art big data analytic methods. Such techniques include clustering algorithms, pattern based information extraction, linear and non-linear regression, and feature based models. Inevitably, much work on big data analysis is statistical, so you will therefore develop some relevant statistical understanding. As data visualization is frequently critical in helping to develop hypotheses about the data, you will also cover and apply problem relevant 2D and 3D visualization methods where appropriate to the particular datasets.

In this module, you will learn data science lifecycle and foundations, principles, and fundamental statistical methods, techniques and applications in data science. You will explore key areas of data science including question and hypotheses formulation, data collection and cleaning, visualization, statistical inference, predictive modelling, and decision-making. You will learn fundamental aspects of probability and statistics to equip you to lead standard data analysis projects in industry and research. The module will cover broad topics such as:

• Foundations of Data Science
• Principles and techniques of Data Science
• Review and evaluation of Data Science methods, techniques and tools

In this module, you will learn about the entire data life cycle (from creation to disposal) and will gain a deep understanding of classical database development processes and approaches to modelling, design and management of databases. You will be able to learn and employ data warehousing techniques to integrate and consolidate data from different sources, which can then be used for business reporting, exploratory data analysis and advanced data analytics. In addition, you will realise the responsibilities of database designers with respect to professional, legal, security and ethical issues as well as undertaking risk management and evaluation of commercial risk in relation to data management. Moreover, you get an appreciation of non-traditional data types, systems and applications (e.g., NoSQL Databases), data standards and data quality. The module will covers topics such as:

• An overview of the entire data life cycle (e.g., creation, modelling, representation, usage, maintenance, disposal, etc)
• Classical data engineering processes and approaches (modelling, design, implementation and management and access of databases)
• Data warehousing
• Non-traditional data management technologies (e.g., NoSQL databases)
• Data analytics
• Data standards and data quality

The aim of this module is to provide you with the knowledge and practical skills for understanding the statistical methods and programming for data science. The module combines both theoretical and practical approaches so that you will have the skills to tackle problems in various realistic business settings. It also equip you with programming skills in R for effective and efficient statistical data analysis.

This module is primarily concerned with examining and analysing data using R (statistical programming) arising from real world environments (e.g., businesses, industries) and to relate the extracted information to strategic, tactical and operational decision-making. You will covers topics such as:
• Introduction to statistical modelling, data processing and big data and to challenges in practical data analyses
• Fundamental statistics: variable types: nominal, ordinal, categorical. Formulae: functions, powers, summation
• Introduction to R programming language environment
• Introduction to practical data analysis with the statistical software environment R for data manipulation, sampling, importing and exporting data and for drawing Scatterplot, Histogram, etc.
• Application and implementation of core statistical analysis using R (e.g., probability and distributions, Statistical models (e.g., Linear models, Generalized linear models, Nonlinear least squares and maximum likelihood models)
• How to subsume and to present results of statistical data analyses (for statisticians and non-statisticians)
• Practical statistical analysis of real data sets, reporting and presentation of the obtained results

Academic skills when studying away from your home institution can differ due to cultural and language differences in teaching and assessment practices. This module is designed to support your transition in the use and practice of technical language and subject specific skills around assessments and teaching provision in your chosen subject area in the Department of Architecture and Built Environment. The overall aim of this module is to develop your abilities to read and study effectively for academic purposes; to develop your skills in analysing and using source material in seminars and academic writing and to develop your use and application of language and communications skills to a higher level.

The topics you will cover on the module include:

• Understanding assignment briefs and exam questions.
• Developing academic writing skills, including citation, paraphrasing, and summarising.
• Practising ‘critical reading’ and ‘critical writing’.
• Planning and structuring academic assignments (e.g. essays, reports and presentations).
• Avoiding academic misconduct and gaining credit by using academic sources and referencing effectively.
• Listening skills for lectures.
• Speaking in seminar presentations.
• Giving discipline-related academic presentations, experiencing peer observation, and receiving formative feedback.
• Speed reading techniques.
• Discussing ethical issues in research, and analysing results.
• Describing bias and limitations of research.
• Developing self-reflection skills.

In this module you will develop knowledge and skills that will enable you to tackle a realistic machine learning problem, using some of the principal advanced machine learning techniques. You will also learn about recent applications of machine learning. Furthermore, you will learn how to implement machine learning based solutions and evaluate their performance using real world examples. The main topics covered in this module include:

• Mathematical foundations of machine learning
• Supervised, Unsupervised and reinforcement learning
• Feature extraction, feature selection and dimensionality reduction
• Classification and clustering techniques
• Optimisation techniques
• Ensemble techniques
• Autoencoders
• Deep generative models
• Deep Learning
• Data visualisation