MSc Data Science

Program Overview

The MSc Data Science program at Liverpool John Moores University equips students with industry-standard tools and methodologies to excel in data science. The program covers statistical techniques, machine learning algorithms, and computational methods for analyzing large datasets. Students complete an extended research project under the guidance of world-leading researchers, preparing them for careers as qualified Data Scientists or further research.

Program Outline

Degree Overview:

The MSc Data Science program at Liverpool John Moores University is designed to equip students with the skills and knowledge necessary to excel in the field of data science.

Objectives:

• Gain a comprehensive understanding of industry-standard tools and methodologies used in data science.
• Develop the ability to apply advanced statistical techniques and machine learning algorithms to solve real-world data science problems.
• Conduct an extended research project using real-world data science problems.
• Learn from world-leading researchers in the field.
• Prepare for a career as a qualified Data Scientist or pursue further research through a PhD or equivalent.

Program Description:

The program comprises six core modules covering statistical and computing techniques, plus an extended research project. It is delivered through a combination of lectures, tutorials, and hands-on computer laboratory sessions. A significant component of the MSc program is the research project module, which provides students with the opportunity to work on a high-level original research topic under the guidance of an experienced researcher and supervisor.

Outline:

Program Structure:

• The MSc Data Science program is available for study as either a 180 credit or 240 credit routeway.
• The 180 credit version of the course is typically completed over one full year, with taught modules in Semesters 1 and 2, and the independent research project in the summer.
• The 240 credit routeway is studied over two years, with Semesters 1 and 2 in both years.
• The 240 credit route includes a Group Design Project that aims to apply data science to the solution of a real-world problem.

Course Schedule:

• Semester 1:
• Introduction to Data Analytics (20 credits)
• Statistical Methods in R (20 credits)
• Big Data Computing (20 credits)
• Semester 2:
• Research Methods in Data Science (20 credits)
• Machine Learning and Data Mining (20 credits)
• Efficient algorithms for complex data sets (20 credits)
• Summer:
• Statistical Methods in R: This module provides an introduction to key concepts in statistics and statistical computing using the R programming language, with an emphasis on the informed interpretation of the results of statistical testing.
• Big Data Computing: This module develops skills in modern computing techniques for high-performance analysis of large data sets.
• Research Methods in Data Science: This module equips students with the research skills necessary to undertake their dissertation project.
It covers the preparation of a research proposal or a business case, consideration of ethical and legal issues, literature review, oral and written presentation skills, and project management.
• Machine Learning and Data Mining: This module develops skills in data mining, using methods from computational learning theory and artificial intelligence to extract previously unknown relationships from large data sets.
• Efficient algorithms for complex data sets: This module develops skills in big data analysis, including techniques of dimensionality reduction and the application of statistical and machine learning models.
It also aims to develop the key skill of working with experts from other domains.
• Data Science Project: This module allows students to display all of the skills learned on the program and demonstrate creativity, initiative, ingenuity, and communication in an academic context.
• Work Based Project (Optional): The Work Based Project aims to apply data science to the solution of a real-world problem, which will help to consolidate knowledge and skills learned using the tools and systems that data scientists and data engineers use.

Assessment:

• Assessment methods include exams, coursework, and an extended report based on the dissertation project.
• Typically, 50% of the mark comes from coursework and 50% from an exam, although some modules may be assessed entirely by coursework done in the computer lab.

Teaching:

• Teaching methods include lectures, seminars, practicals, directed reading, focused literature review exercises, coursework assignments, student discussion groups, and tutorials.
• Analytic, mathematical, and problem-solving skills are developed through self-learning assessments, focused literature review exercises, written assignments, and tutorials.
• The Research Project involves both experimental and literature research, further developing skills of research methodology, critical analysis, and discussion.
• Practical experimental skills, preparing a high-level technical report, and giving a technical presentation are developed through the Research Project and Research Methods modules.
• Computational and programming skills are embedded in every module and will be developed throughout the program.
• Transferable skills are embedded on the program.
Skills are learned through completion of written assignments, participating in tutorials, collecting and transferring data via computer networks and from online databases, and through the Data Science Project.
• As part of the research methods and project modules, students will be encouraged and assisted to develop a professional online presence.

Careers:

• On graduation, students may become qualified Data Scientists or carry out further research at doctorate level.
• Data Science is a growing area of demand with a predicted scarcity of expertise forecast over the next decade.
• LJMU has strong links with many graduate employers of data scientists who value the skills developed in the course, such as accessing, manipulating, and drawing insight from large volumes of complex data; assimilating information from the research literature and scientific and technical writing.

Other:

• The Astrophysics Research Institute in the Knowledge Quarter combines world-leading research in astrophysics with excellence in teaching at all levels.
• The Department of Applied Mathematics, based in the City Campus, is dedicated to producing research with real-world applications and offers a student-centered teaching portfolio.
• Teaching on this program takes place in the city center.

• Home full-time per year: £9,840
• International full-time per year: £18,250
• International year 2: £5,630