MSc Data Science with Placement Year

University Of Hertfordshire

MSc Data Science with Placement Year

  • Country United Kingdom
  • Course Duration 24 month
  • Course Type Full Time
  • Courses Campus On Campus
  • Language Specification IELTS
  • Program Level Post-Graduate
  • Education Required Undergraduate
  • Admission intake SEP
  • Minimum GPA 3.2
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Application Charges

Application Fee Tution Fee
Free GBP 16,500

Application Date

Application Start Date Application Closing Date
2023-02-01
2023-08-16

Program Description

Data is the currency of all but the most theoretically-based scientific research, and it also underpins our modern world, from the flow of data across international banking networks and the spread of memes across social networks, to the complex models of weather forecasting. The constant generation of data from our digital society feeds into our everyday lives, affecting how we receive healthcare to influencing our shopping habits. In order to handle, make sense of, and exploit large volumes of available data requires highly skilled human insight, analysis and visualisation. The professionals working in this field are called ‘data scientists’, who blend advanced mathematical and statistical skills with programming, database design, machine learning, modelling, simulation and innovative data visualisation. These professionals are in high demand in both public and private sectors in the UK and worldwide. This programme aims and learning outcomes are built around two guiding principles:
  • To provide comprehensive understanding of the fundamental mathematical and statistical concepts underlying data science, and how they are implemented in algorithms and machine learning techniques to solve a variety of data processing and analysis problems.
  • To provide training in the practical skills relevant to data science, central of which is the ability to write clean and efficient code in industry-recognised languages (in particular, Python and R), but also includes data handling, manipulation, mining and visualisation techniques.

Why choose this course?

  • This programme is distinctive in its philosophy of widening participation and provides a route to gain skills and training in data science to those from a background not traditionally associated with the STEM-themes of mathematics, statistics and programming. The programme is designed to be appealing to a broad range of students who are seeking training or up-skilling in data science.
  • You will benefit from the expertise of astrophysicists, physicists, mathematicians and computer scientists with international research profiles. Their day-to-day research involves application of, and in some cases the development of new, data science skills, from fundamental statistical analyses, the use of distributed high-performance computing, and research into novel artificial intelligence algorithms.
  • We aim to make the programme distinctive in terms of the mixture of hard and soft skills, and the close personal relationship that we are developing with employers, which will feed into the programme through continuous assessment of the latest industry-relevant tools, which are continually evolving as new technology and software becomes available.
  • You will experience a multidisciplinary approach to data science by experiencing challenges in computer science, creative arts, medical and business environments.
  • You will have the opportunity to attend a wide range of research-focused seminars to excite and spark your intellectual curiosity.

What will I study?

The curriculum is structured to ensure are exposed to the fundamental mathematical and statistical principles underpinning all data science. These themes will always be relevant in what is a constantly evolving field. Theoretical work will be reinforced with practical application through hands-on laboratories and workshops, to enable you to understand and appreciate how fundamental principles are reflected in a broad range of data processing and analyses. You will become proficient in key practical skills (e.g. use of pandas for working with data structures within Python, and ggplot2 for visualisation in Python and R) using ‘real-world’ data where possible. In some cases, this data can be sourced from active research projects being conducted by members of the teaching staff.The programme focuses on providing ‘end-to-end' training so that you become competent not only in the processing and analysis of data but also in manipulating and preparing data from a raw state as well as interpreting results and effectively communicating findings to others. This will enable you to be prepared for real-world challenges and applications and will help you to develop independence in your analytical and critical thinking. This will be nurtured in laboratory-based practical sessions so you can put your theories into practice.

Other Requirements

The normal entry requirements for the programme are a good (2.2 or above) Honours Degree (or equivalent) in a STEM subject (e.g. Mathematics, Engineering, Physical Sciences etc).

Extra Notes


* NOTE : The Tuition Fee is subject to change Semester wise, as such for exact Tuition Fee kindly see the Website of the respective University.
* NOTE : Pathways : Students who want to pursue a Graduate Degree through a particular University but are unable to meet the university requirements for the GPA, GRE/GMAT or English Language scores, have to take up an additional academic and language support i.e. pathways in order to ensure the admit and study at the desired University.