MSc Data Science and Analytics with a Year in Industry

2 Years On Campus Masters Program

Royal Holloway University of London

Program Overview

The MSc in Data Science and Analytics at Royal Holloway provides advanced training in programming, statistics, data management and machine-learning techniques for working with large and complex datasets. It suits students with quantitative or computing backgrounds who aim for careers in data science, analytics, or data-focused research roles.

Curriculum Structure (Full-time, 2 year)

Year 1- Students begin with core modules such as Data Analysis, Programming for Data Analysis, Database Systems, Large-scale Data Storage and Processing, and Computation with Data, which develop strong foundations in analytics, coding, databases and scalable data processing. They then choose from optional modules like Machine Learning, Online Machine Learning, Methods in Bioinformatics, or Microeconometrics, allowing them to specialise in predictive modelling, big-data workflows, biological data, or economic data analytics.The programme concludes with an Individual Project / Dissertation, where students apply analysis, modelling and interpretation skills to a substantial real-world or research-based data-science challenge.

Year 2- You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement.

Focus areas:
“Data programming; statistical analysis; databases; large-scale data processing; machine learning; domain specialisation; applied data-science project”

Learning outcomes:
“Manage and analyse large datasets; apply statistical and machine-learning methods; build scalable data workflows; interpret and communicate insights; conduct ethical and rigorous data-science research; complete a full data-science project”

Professional alignment (accreditation):
Designed to meet industry demand for data scientists, analysts and data engineers, preparing graduates for roles across technology, finance, healthcare, research and public sector analytics.

Reputation (employability rankings):
Royal Holloway’s Computer Science department is well regarded for research excellence, giving graduates strong credibility and high employability in data-driven industries.

Experiential Learning (Research, Projects, Internships etc.)

Students gain practical skills through a combination of project-based learning and access to the University's high-performance computing resources, applying machine learning algorithms to complex datasets from various domains. The programme emphasises both the theoretical foundations and practical implementation of machine learning, with a strong focus on developing robust, data-driven solutions. This applied learning is central to the curriculum and is delivered through:

Core Software & Programming: Intensive use of Python and R, with deep exposure to key machine learning and data science libraries including Scikit-learn, TensorFlow, Keras, and Pandas.
Computing Facilities: Access to the University's High-Performance Computing (HPC) cluster, which provides the necessary computational power for training complex models on large-scale datasets.
Research Project: A substantial individual MSc dissertation project that allows students to pursue a specialist area, often involving the application of machine learning to a novel problem or the development of new methodologies.
Group Projects: Collaborative data analysis and software development projects where teams work on end-to-end machine learning solutions.

Progression & Future Opportunities

Graduates of Royal Holloway's MSc Data Science and Analytics secure high-demand roles due to skills in machine learning, big data tools like Hadoop/R, and analytics across sectors like healthcare and finance. Proximity to England's Silicon Valley enables placements at top firms, boosting employability with buoyant global demand and salaries double the UK average. Alumni join companies like Amazon, Google, Microsoft, and JP Morgan.

Typical job roles: Data Scientist, Data Analyst, Machine Learning Engineer, Business Intelligence Analyst.