menu icon
Coronavirus (COVID-19) Latest news
MSc Data Science with Professional Placement

MSc Data Science with Professional Placement

Different course options

Study mode

Full time


2 years

Start date


Key information

Qualification type

MSc - Master of Science

Subject areas


Course type


Course Summary

The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:

  • Computer science
  • Programming
  • Statistics
  • Data analysis
  • Probability

A successful career in data science requires you to possess truly interdisciplinary knowledge, so we ensure that you graduate with a wide-ranging yet specialised set of skills in this area. You are taught mainly within our Department of Mathematical Sciences and our School of Computer Science and Electronic Engineering, but also benefit from input from our Essex Business School, and our Essex Pathways Department. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course can open the door to almost any industry, from health, to government, to publishing.


The module introduces decision theory, hypothesis testing, "Monte Carlo" simulation, Bayesian inference, comparative inference and the generalised linear model. On completion of the course students should be able to (learning outcomes): Understand concepts of decision theory; Understand hypothesis testing, exact and asymptotic tests, properties of tests; Understand basic principles of Bayesian inference; Understand principles and methods to choose good estimators; Understand basic concepts of a generalised linear model.

Tuition fees

UK fees
Course fees for UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


International fees
Course fees for non-UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


Entry requirements

A 2:2 degree in one of the following subjects: Mathematics, Statistics, Operational research, Finance, Economics, Business Engineering, Computing, Biology, Physics or Chemistry. Will consider applicants with a unrelated degree but which contained at least three modules in calculus, algebra, differential equations, probability & statistics, optimisation or other mathematical modules.