Data Science (with optional Placement/Project year) MSc

An essential skill for postgraduate students is the ability to investigate topics with the objective of identifying facts , theories, ideas, methodologies, etc., that could inform the development of new insight for further research.  A major aspect of this is critical analysis of information. The module aims to develop critical reasoning in students and an understanding of other researchers’ work.  Students will learn how to use current research literature and relevant sources to gain new insight for a new research.  They will learn how to support their research report with relevant facts, theories, ideas, etc.  They will develop their ability to understand approaches and methodologies adopted in existing research toward writing a literature review and handling full research projects in their subject area.

The learning content also includes:

• Time management, library skills and literature search
• Evaluation of information sources
• Ethical issues in science, technology and engineering research (including intellectual property and plagiarism)
• Writing for research: styles and rules for presentation (including referencing standards)
• Choosing a research area and evaluating source material
• Hypothesis formation
• Design and application of questionnaires & interviews
• Quantitative and statistical tools for researchers (e.g. R, Python, SPSS)

The module introduces the basic concepts of programming for the purpose of statistical analysis. It explores data structures such as lists, dictionaries, and arrays and functions to calculate min, max, mean, and standard deviation.

The mathematical and statistical skills include statistics and probability, multivariate calculus, linear algebra and optimisation methods.

The topics covered include:

Programming Concepts

• Data structures: lists, dictionaries, arrays.
• Functions: min, max, mean, standard deviation.
• Programming logic and debugging.
• Data visualization: Creating visualizations using tools like matplotlib, ggplot2, and seaborn.
  Big data concepts: Basics of working with large datasets and addressing scalability challenges.

Statistics

• Descriptive statistics.
• Probability theory.
• Inferential statistics.
• Exploratory data analysis.
• Probability distributions: Understanding normal, binomial, and Poisson distributions and their applications.
• Time series analysis: Introduction to time series methods such as moving averages and ARIMA.Linear Algebra

Linear Algebra

• Vectors and matrices.
• Eigenvalues and eigenvectors.
• Linear systems.

Optimisation Methods

• Unconstrained optimisation.
• Constrained optimisation.
• Applications in statistical modelling and machine learning.

This module investigates tools and techniques to extract, transform and load (ETL) data for further analyses or analytical processing (OLAP). Students will be guided step-by-step through the ETL process using Python, API's and SQL to show visualisation and analysis. The module will also discuss the ethical implications of data, data processing, laws and standards. 

This module investigates different types of machine learning algorithms to find patterns in data. Each algorithm will be discussed in theory and practice, discussing: its data pre-processing requirements, pseudo-code, and evaluation metrics, e.g., Dunn index for clustering. Detailed demonstrations will show how to apply these algorithms to data using specified libraries in Python. Students will be required to investigate the merits of each algorithm for various types of data in both theory and practice.

The databases and security module involves the development of databases and their querying through the use of SQL. Databases will be discussed both theoretically and in practice. Students will have opportunies to develop and practice database creation and development. Database security will be discussed and shown how to apply in practice.

The following topic areas are indicative of the module content:

• Enterprise, entrepreneurship and modern world of work
• Key roles, functions and objectives of successful business enterprise
• Creativity, innovation and growth strategies and approaches
• Business Management approaches to innovation, change and business development
• Exploring, assessing and seizing opportunities
• Business idea, planning and start up

This module introduces students to key approaches, behaviours and skills of successful business enterprise by providing insight into real world business scenarios, key tools and processes required for both developing an existing business and creating a new business venture start up. 

The module promotes a proactive, value added approach to developing commercial skills and knowledge specifically aiming to: 

• Analyse real business scenarios to identify and evaluate feasible and viable business development opportunities within a dedicated sector linked to degree specialism;
• Explore and build knowledge of theoretical approaches to innovation, business start up and operations;
• Apply learning and practical knowledge of sound business enterprise characteristics, and behaviours to assess opportunities, select a viable option and develop a proposal for a new business venture concept.

The Research Project is the pinnacle of a taught, academic programme of master’s level study. It is a demonstration of academic, subject-specific, and research capabilities. Projects are a significant and substantial piece of individual work that draw upon the knowledge, technical abilities, and problem-solving skills developed in earlier modules. Students need to apply high-level research skills to a defined, complex problem. A distinguishing feature of the research project is that it is largely self-directed and independent, with support from an academic supervisor.