Introduction to Advanced Data Science and Artificial Intelligence MSc

The Advanced Data Science and Artificial Intelligence MSc program is designed to prepare students for employment in the IT industry, where big data professionals are in high demand. The program focuses on hands-on experience with big data analytics, data mining, and visualization techniques using high-performance computer technology.

Course Content

The program covers theoretical and practical aspects of programming for the latest multi-core and multi-processor platforms, key algorithms, approaches, and software environments for developing solutions to big data problems, and data mining techniques and challenges using real-world datasets. Students will also learn about the application of visualization methods to data mining, research skills in computer science, bio-inspired algorithms for optimization and machine learning, and how to model continuous and discrete optimization problems.

Course Structure

The course is available to start in September or January and is typically completed in 12 months. The program consists of taught modules and a final project. In the first semester, students will study two compulsory taught modules and two optional taught modules. In the second semester, students will study two compulsory taught modules and two optional taught modules.

Compulsory Modules

• RESEARCH METHODS IN COMPUTER SCIENCE (COMP516)
• BIG DATA ANALYTICS (COMP529)
• MSC GROUP PROJECT (COMP530)
• DATA MINING AND VISUALISATION (COMP527)

Optional Modules

• EFFICIENT ALGORITHMS (COMP526)
• MULTI-CORE AND MULTI-PROCESSOR PROGRAMMING (COMP528)
• QUANTUM COMPUTING AND SECURITY (COMP535)
• OPTIMISATION (COMP557)
• ONTOLOGIES AND SEMANTIC WEB (COMP318)
• SAFETY AND DEPENDABILITY (COMP524)
• MACHINE LEARNING AND BIOINSPIRED OPTIMISATION (COMP532)
• COMPUTATIONAL INTELLIGENCE (COMP575)
• ALGORITHMIC GAME THEORY (COMP559)

Teaching and Assessment

Teaching comprises formal lectures, small group tutorials, and practical sessions in computer laboratories. Students may also take part in one or more group projects. In addition, students complete an individual project under academic supervision. Taught modules are assessed through a combination of examinations and coursework.

Careers and Employability

The program is designed to address a skills gap in the employment market and will enable students to apply their skills working with big data and their knowledge of high-performance computing to real-world challenges. Examples of relevant careers include data analyst, data scientist, mathematical modeller, database administrator, machine learning engineer, and statistician.

Fees and Funding

Tuition fees for the program are ｣14,000 for UK students and ｣34,000 for international students. Students may be eligible for scholarships and bursaries, including the Graduate Loyalty Scholarship, the Salters' Fellowship, and the Bracken Scholarship.

Entry Requirements

To apply for the program, students must have a 2:2 honours degree from a UK university or an equivalent academic qualification from a similar non-UK institution. This degree should be in computer science or a closely related subject. International students must also demonstrate competence in the use of English language, unless they are from a majority English-speaking country.

English Language Requirements

Students must have an IELTS score of 6.5 overall, with no component below 5.5, or an equivalent score in another English language test. Students who do not meet the minimum required standard of English language can complete a Pre-Sessional English course to achieve the required level.

Accreditation

The program starting in September is accredited by BCS, The Chartered Institute for IT, for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. The program starting in January is pending accreditation by BCS, The Chartered Institute for IT.

University of Liverpool