Postgraduate study
Computing & Web

MSc Artificial Intelligence with Data Analytics*

he MSc Artificial Intelligence with Data Analytics course is designed for graduates seeking to build on your existing skills to develop specific expertise in the field of artificial intelligence and data analytics.

Course information

Full-time

  • within 1 year (September start), 16 months (January start) or 2 years with advanced practice (September or January start)

More full-time details

2019 entry

Part-time

  • Up to 3 years (can be completed in 2 years if preferred)

More part-time details

2019 entry

Contact details

Further information

  • Facilities

    Computing and Web

    Teesside has fantastic state-of-the-art facilities for web and computing students including a wide range of web, multimedia, network and programming studios. This environment prepares students for work in industry by promoting team work and the use of case studies, problem solving and methods such as peer programming.

 

You strengthen and deepen your skills on the cutting edge of computer science and are prepared to make the transition from programmers to team leaders and designers. Particular features include:

  • In addition to major themes of artificial intelligence and data analytics, you also focus on a supporting strand of statistical methods and research methods to provide the academic rigour required for postgraduate study and the practical skills for entry to industry.
  • You experience new trends pervading the software industry that influence a wide range of applications, from supply chain analysis to pharmaceutical manufacture.
  • You benefit from a range of authentic and engaging learning experiences and assessments.
  • It is particularly suited to overseas students who wish to develop practical and cutting edge skills for entry to their local computer science industry.
  • The fixed module diet presents a unique course that encapsulates leading edge skills, solid programming experience, research expertise and industry experience.

If you take the two year course the Advanced Practice (internship) provides an opportunity to improve employment prospects by providing real-world experience to develop new skills and a deeper understanding of the subject.

Course structure

Core modules

Advanced Data Analytics

This module provides you with the core principles and practical skills to apply state-of-the-art computational methods to perform data analytics. The skills are very important in the new horizon of data analysis where existing massive amount of data contains valuable knowledge, which is critical for prediction and decision-making. Due to its characters (3V: volume, velocity, and variety), computational methods are required to extract such knowledge.

You form a solid foundation of 1) predictive analytics, 2) data-driven decision making which refers to tools and techniques for building statistical or machine learning models to make predictions and decisions based on data. Practical guidance about how to handle unlabelled, noisy, incomplete, large-scale data is discussed and you learn how to select the best technique to handle different type of data in different scenarios.

Intelligent Agents

This module examines knowledge-based artificial intelligence (AI), which is concerned with building and deploying logic-based inference, planning and decision making. The focus of knowledge-based AI has become increasingly interested in building intelligent agents – independent entities that perceive their environment and take actions to maximise the chances of achieving their goals. These agents range from simple, reactive forms to complex cognitive decision-makers, often residing in social spaces with other agents. You examine the tools and techniques used to engineer intelligent systems (agent-based and other) and present an in-depth study of key research and application areas of intelligent systems.

Intelligent Systems Programming

You examine modern approaches to building symbolic artificial intelligence (AI) inference mechanisms into software applications, contrasting simple reactive systems with those whose behaviours are plan-based and cognitive. The module has a strong practical underpinning investigating the semantics of Clojure: a modern functional and symbolic programming language for the java virtual machine. It builds on earlier studies of programming and algorithms to bridge the gap between theoretical understanding and implementation developing those advanced programming skills necessary to construct and evaluate knowledge-based AI software.

Machine Learning

Machine learning is a subfield of computer science concerned with computational techniques rather than performing explicit programmed instructions. You build a model from a task based on observations in order to make predictions about unseen data. Such techniques are useful when the desired output is known but an algorithm is unknown, or when a system needs to adapt to unforeseen circumstances.

You explore statistics and probability theory as the fundamental task is to make inferences from data samples. The contribution from other areas of computer science is also essential for efficient task representation, learning algorithms, and inferences procedures. You gain exposure to a breadth of tasks and techniques in machine learning.

Assessment is an in course assessment (100%).

Master's Project: Artificial Intelligence/ Data Analytics

Research Methods

You gain the knowledge and skills to understand the research process in computing and digital media, and the necessary skills to undertake your masters project. You learn how to use and critically evaluate previous academic research, and to generate good evidence material to justify their professional practice. This involves you learning about different research strategies and data generation methods and how they fit into the development lifecycle and the evaluation of the user experience, the use of the academic research literature, and research ethics.

Assessment involves you preparing a research proposal which can form the basis of your master's project.

Statistical Methods

You develop necessary knowledge and practical understanding of the main statistical techniques. You explore quantitative and qualitative data analysis techniques, reflecting scientific and social science methods. You focus on correlation testing, regression, data categories, normalization - the tools needed, rather than the philosophical approaches. You understand how to apply valid techniques and interpret the results in preparation for experimental work.

Your assessment is a single ICA based around a number of case studies that require you to identify the correct data analysis and modelling processes.

 

Advanced practice

Internship

The internship options are:

Vocational: spend one semester working full-time in industry or on placement in the University. We have close links with a range of national and international companies who could offer you the chance to develop your knowledge and professional skills in the workplace through an internship. Although we cannot guarantee internships, we will provide you with practical support and advice on how to find and secure your own internship position. A vocational internship is a great way to gain work experience and give your CV a competitive edge.

Research: develop your research and academic skills by undertaking a research internship within the University. Experience working as part of a research team in an academic setting. Ideal for those who are interested in a career in research or academia.


 

Modules offered may vary.

Entry requirements

For this particular course, there may be a need for you to undertake an occupational health/work-based risk assessment. If you have a disability, specific learning difficulty, mental health condition, autism spectrum condition, sensory impairment or medical condition that may require reasonable adjustments during an external placement or in the university or in a clinical practice area, this must be declared as part of the enrolment process. If you are unsure you can contact the relevant admissions or course tutor for guidance.

For additional information please see the entry requirements in our admissions section

International applicants can find out what qualifications they need by visiting Your Country

* Subject to University approval

Course information

Full-time

  • within 1 year (September start), 16 months (January start) or 2 years with advanced practice (September or January start)

More full-time details

2019 entry

Part-time

  • Up to 3 years (can be completed in 2 years if preferred)

More part-time details

2019 entry

Contact details

Further information