Data Science MSc

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
• Leadership / 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 that offer value to the business owner;
• Explore and build knowledge of theoretical approaches to innovation, leadership, management, business operations, and commercial acumen;
• Apply learning and practical knowledge of sound business enterprise characteristics and traits to 1) developing an existing business and 2) developing a new business venture concept.

The module introduces the basic concepts or programming for the purpose of statistical analysis. It explores data structures and functions that may be used to store and analyse data.

Python/R:

• Data structures such as lists, dictionaries, and arrays
• Functions to calculate min, max, mean, and standard deviation

Mathematical and Statistical Skills

• Statistics and probability
• Multivariate calculus
• Linear algebra
• Optimisation methods

The module aims to enable students to learn statistical and mathematical tools and techniques that are of interest when analysing, processing and visualising data sets.

1. Projects which will involve the application of methods and equipment introduced in taught modules, will be based on subjects agreed in principle with the Postgraduate Dissertation Coordinator and potential supervisors.
2. The research dissertation may be University-based or carried out in the employer’s workplace, or through a work placement where a local organisation has a direct role in facilitating the project.

To afford students the opportunity to experience the complete life-cycle of a successful and significant research-based project

To provide real-world experience of meeting the requirements of academic and professional standards, including high-level writing and referencing skills.

To demonstrate to peers and to current and potential employers the student’s ability to carry out good quality academic research, in a particular field, which is relevant to their programme of study. This may involve the application of existing research within a novel context.

To include:

• Time management, library skills and literature search
• Evaluation of information sources
• Critical analysis of information
• 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
• Research approaches and methodologies
• Design and application of questionnaires & interviews
• Quantitative and statistical tools for researchers (e.g. R, Python, SPSS)

• To clarify the distinctions between undergraduate and postgraduate level work and expectations
• To increase students' experience in order to conduct a professional study and to use sampling procedures and analysing techniques.
• To improve students' appreciation of time management and how to conduct a literature search
• To reinforce students' research skills
• To consolidate students' appreciation of professional issues such as copyright and ethics

This module investigates tools and techniques to extract, transform and load (ETL) data into a data warehouse for the purpose of online analytical processing (OLAP). Students will be guided through step-by-step demonstrations showing them how to perform the ETL process using a suitable tool, such as Python or R. Tools such as SQL and Excel will be used to demonstrate extracting data for visualisation and analysis, e.g., building a data cube. Additionally, the module will investigate alternative approaches to data warehousing, e.g., the Hadoop ecosystem.

This module introduces concepts of data science as a discipline and develops students skills in the areas tacking the manipulation of data such as loading, transforming and storing data.

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 on data using specified libraries in either Python or R. Students will be required to investigate the merits of each algorithm for various types of data in both theory and practice.

Students in this module will learn how to use, apply and develop machine learning tools for data science applications.

• What are ethics?
• Ethical frameworks for data science.
• Privacy/confidentiality (right to be forgotten, surveillance, anonymity,
  etc.)
• Transparency and bias (data driven bias, opaque decision making,
  etc.)
• Rights and responsibilities (human rights, machine rights, automated decision making, self-driving cars and warfare,
  etc.)
• Information and dis-information (democracy and social media,
  etc.)

This module aims to introduce students to ethics as applied to the fields of data science, machine learning and artificial intelligence. It explores these theories in the context of real world problems including accountability, ethical availability of data, enhancing the value of data, and ensuring accountability, transparency and oversight.