MSc Data Analytics and Technologies ( University of Greater Manchester)
Entry Requirement
The following modules will be covered in the MSc Data Analytics and Technologies degree course, carrying a total of 180 credits.
Course Structure
Credits: 20 – Module Type: Core
This module develops an understanding of the professional and legal constraints within which computing specialists operate. The module operates using a ‘discursive’ environment where you will be confronted with the social and ethical issues of using technology, considering both regional and global trends and perspectives. Skills learnt will include how to communicate your work effectively keeping in line with the process and standards to be adhered to as a Professional in Computing Field. The module helps to develop a mature attitude to working as an ethical, sustainability aware, computer or information systems professional as well as build upon your undergraduate research skills through the introduction and application of advanced techniques to support you in your professional practice. The following GAME+ attributes are covered in this module: Influence and impact, and Critical self-management.
Credits: 20 – Module Type: Core
This module allows you to develop skills, knowledge and techniques to improve your capabilities in research and to deal with complex issues systematically and creatively. This includes investigating processes to help evaluate and critically appraise both qualitative and quantitative research papers; identify, select and utilise different approaches to literature searching and review; identify appropriate epistemological and methodological approaches that will underpin your research design; and formulate and develop achievable research aims, questions and objectives. This module builds on previous learning focused on gaining insights from academic literature and formulating research project proposals. Knowledge gained in this module will be consolidated and extended in the capstone project module while writing the dissertation and preparing for your viva voce. The following GAME+ attributes are covered in this module: Influence and Impact, and Critical Creativity and Innovation.
Credits: 20 – Module Type: Core
This module aims at developing advanced knowledge and skills in data analysis and visualisation. The module will be delivered in a lab environment where sessions grounded in theory are underpinned by practical labs. These practical lab sessions leverage real-world data for analysis and visualisation using contemporary tools in the industry. This module builds upon previous learning focused on strategies for leveraging data for insight and innovation. Knowledge gained in this module will be consolidated across other modules in the programme and extended in the capstone project module depending on dissertation theme and area of specialisation. The following GAME+ attributes are covered in this module: Critical Creativity and innovation and Skills mastery.
Credits: 20 – Module Type: Core
This module covers the use of Big Data frameworks and Cloud technologies for effective manipulation and analysis of large data sets. Students will be exposed to core Big Data analytics concepts and models, contemporary technologies, as well as develop skills to structure and analyse structured, semi-structured, and unstructured data using Big Data tools such as Hadoop and Spark. This module builds upon previous learning focused on strategies for leveraging data for insight and innovation. Knowledge gained in this module will be consolidated across other modules in the programme and extended in the capstone project module depending on dissertation theme and area of specialisation. The student will able to perform CRUD operations and query relational and non-relational databases using SQL and NoSQL. The following GAME+ attributes are covered in this module: Influence and Impact, and Critical Creativity and Innovation.
Credits: 20 – Module Type: Core
This module introduces students to the principles, theories, and applications of data mining and machine learning techniques. Students will learn about different algorithms and techniques for analysing large datasets and building predictive models. The module will cover techniques for data preprocessing, feature selection, and model selection, as well as model evaluation and interpretation. The module will also address ethical considerations and current challenges associated with using data mining and machine learning techniques. This module builds upon previous learning focused on strategies for leveraging data for insight and innovation. Knowledge gained in this module will be consolidated across other modules in the programme and extended in the capstone project module depending on dissertation theme and area of specialisation. The following GAME+ attributes are covered in this module: Professional identity and Skills mastery.
Credits: 20 – Module Type: Offered
This module introduces students to the concepts of business analytics. students will learn the fundamentals required to analyse time series data, perform time series forecasting using various statistical, machine learning, and deep learning techniques. The module will also cover the optimisation techniques for problem solving. This module builds upon previous learning focused on strategies for leveraging data for insight and innovation. Knowledge gained in this module will be consolidated across other modules in the programme and extended in the capstone project module depending on dissertation theme and area of specialisation. The following GAME+ attributes are covered in this module: Professional identity and Skills mastery.
Credits: 60 – Module Type: Core
This module enables the demonstration of research capability and application of advanced technical knowledge relating to a relevant aspect of the subject pathway. Students first determine the theme of the project which is then evaluated by the assigned research supervisor to ensure that it meets the required academic standards before work is undertaken. This module requires significant use of academic skills and knowledge of research practice, which is crucial for investigating literature and presenting findings, determining appropriate research methodologies and methods, conducting research activity and presenting outputs. Ethics and professional practice is to be adhered to throughout the project. This module culminates with the submission of substantial dissertation and the verbal presentation of significant aspects of work in a viva voce, which is conducted to a professional standard. The following GAME+ attributes are covered in this module: Influence and impact, Critical self-management, Critical Creativity and innovation, Professional identity and Skills mastery.
Career Progression
As a graduate of this master’s degree, you’ll possess an impressive all-round combination of skills and knowledge. In addition to a strong theoretical and practical knowledge of data analytics and technologies, you’ll be able to demonstrate essential interpersonal and people skills, such as collaboration and team working, negotiation and persuasion. You’ll have a clear understanding of the contexts in which data analysts work, coupled with commercial awareness and business-relevant knowledge. You’ll also possess high-level academic skills in research, critical thinking and curiosity. Not only will all this help you become established early on in your career, but you’ll also be primed to take on leadership roles. Moreover, you’ll have the lifelong learning, problem-solving and decision-making skills needed to adapt to new challenges throughout your career.
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