MSc Data Science

Program Overview

---

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. Our Department of Mathematical Sciences is genuinely innovative and student-focused. Our research groups are working on a broad range of collaborative areas tackling real-world issues. Here are a few examples:

• Our data scientists carefully consider how not to lie, and how not to get lied to with data. Interpreting data correctly is especially important because much of our data science research is applied directly or indirectly to social policies, including health, care and education.
• We do practical research with financial data (for example, assessing the risk of collapse of the UK’s banking system) as well as theoretical research in financial instruments such as insurance policies or asset portfolios.
• We also research how physical processes develop in time and space. Applications of this range from modelling epilepsy to modelling electronic cables.
• Our optimisation experts work out how to do the same job with less resource, or how to do more with the same resource.
• Our pure maths group are currently working on two new funded projects entitled ‘Machine learning for recognising tangled 3D objects’ and ‘Searching for gems in the landscape of cyclically presented groups’.
• We also do research into mathematical education and use exciting technologies such as electroencephalography or eye tracking to measure exactly what a learner is feeling. Our research aims to encourage the implementation of ‘the four Cs’ of modern education, which are critical thinking, communication, collaboration, and creativity.

This course is available as either a full-time degree over one academic year, or as a part-time degree over two academic years. This course is aimed at candidates with a background in a mathematical or computational discipline. Candidates without strong programming and statistical skills are encouraged to consider our conversion course MSc Data Science and its Applications . Candidates wishing to convert from a non-STEM background are encouraged to consider MSc Applied Data Science . Why we're great.

• We are committed to developing the data scientists of the future.
• Our interdisciplinary Institute for Data Analytics (IADS) researches data issues from the scientific and technological, to the sociological and legal.
• We have active links with industry to broaden your employment potential and placement opportunities.

Our expert staff

Today’s statisticians and computer scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our staff are driven by creativity and imagination as well as technical excellence. We conduct research in areas such as explorative data analysis, classification and clustering, evolutionary computation, data visualisation and financial forecasting. Specialist staff working on data science and analytics include:

• Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
• Professor Berthold Lausen – biostatistics, classification and clustering, data science education, event time data, machine learning, predictive modelling
• Professor Abdel Salhi – data mining, numerical analysis, optimisation
• Dr Spyros Vrontos – actuarial mathematics and actuarial modelling
• Dr Xinan Yang – approximate dynamic programming, Markov decision process

More information about the exceptional and expansive team of data scientists within our department is available on our data science research page.

---

Specialist facilities

• All computers run either Windows 10 or are dual boot with Linux
• Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
• You have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
• We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors
• Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
• The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

---

Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course opens the door to almost any industry, from health, to government, to publishing. Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other areas. Our recent graduates have gone onto work as data scientists and data analysts in both the private and public sectors. We also offer supervision for PhD, MPhil and MSc by Dissertation. We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Program Outline

Course structure

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field. The following modules are based on the current course structure and may change in response to new curriculum developments and innovation. We understand that deciding where and what to study is a very important decision for you. We’ll make all reasonable efforts to provide you with the courses, services and facilities as described on our website. However, if we need to make material changes, for example due to significant disruption, or in response to COVID-19, we’ll let our applicants and students know as soon as possible.

---

---

Components

Components are the blocks of study that make up your course. A component may have a set module which you must study, or a number of modules from which you can choose. Each component has a status and carries a certain number of credits towards your qualification.

Status	What this means
Core	You must take the set module for this component and you must pass. No failure can be permitted.
Core with Options	You can choose which module to study from the available options for this component but you must pass. No failure can be permitted.
Compulsory	You must take the set module for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Compulsory with Options	You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Optional	You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.

The modules that are available for you to choose for each component will depend on several factors, including which modules you have chosen for other components, which modules you have completed in previous years of your course, and which term the module is taught in.

---

---

Modules

Modules are the individual units of study for your course. Each module has its own set of learning outcomes and assessment criteria and also carries a certain number of credits. In most cases you will study one module per component, but in some cases you may need to study more than one module. For example, a 30-credit component may comprise of either one 30-credit module, or two 15-credit modules, depending on the options available. Modules may be taught at different times of the year and by a different department or school to the one your course is primarily based in. You can find this information from the module code . For example, the module code HR100-4-FY means:

HR	100	4	FY
The department or school the module will be taught by. In this example, the module would be taught by the Department of History.	The module number.	The UK academic level of the module. A standard undergraduate course will comprise of level 4, 5 and 6 modules - increasing as you progress through the course. A standard postgraduate taught course will comprise of level 7 modules. A postgraduate research degree is a level 8 qualification.	The term the module will be taught in. AU : Autumn term SP : Spring term SU : Summer term FY : Full year AP : Autumn and Spring terms PS: Spring and Summer terms AS: Autumn and Summer terms

Year 1 The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them. View Introduction to Programming in Python on our Module Directory COMPONENT 02: OPTIOL Option from List A (15 CREDITS) COMPONENT 03: OPTIOL Option from List B (15 CREDITS) View Applied Regression and Experimental Data Analysis on our Module Directory How do you apply multivariate methods? Or demographical and epidemiological methods? And how do you apply sampling methods? Study three application areas of statistics – multivariate methods, demography and epidemiology, and sampling. Understand how to apply and assess these methods in a variety of situations. View Applied Statistics on our Module Directory COMPONENT 06: COMPULSORY WITH OPTIONS CE802-7-AU or MA336-7-SP (15 CREDITS) Relational databases and SQL are developed and used as a fundamental tool for relevant applications from different disciplines including humanities, life sciences, linguistics, marketing and social science. They are essential to the efficient information management for IT systems and commercial applications in almost all modern organisations. The purpose of this module is to provide you with an introduction to the underlying principles and practical experience of the design and implementation of relational databases. It will cover the data modelling and SQL, database analysis, design and management, and advanced topics including big data, security and privacy issues of modern databases. View Databases and data processing with SQL on our Module Directory In a world increasingly driven by data, the need for analysis and visualisation is more important than ever. In this module you will look at data through the eyes of a numerical detective. You will work on the lost art of exploratory data analysis, reviewing appropriate methods for data summaries with the aim to summarise, understand, extract hidden patterns and identify relationships. You will then work on graphical data analysis, using simple graphs to understand the data, but also advanced complex methods to scrutinise data and interactive plots to communicate data information to a wider audience. For data analysis and visualisations you will use R-studio, and a combination of R-shiny applications and google visualisations for interactive plotting. View Data Visualisation on our Module Directory What skills do you need to succeed during your studies? And what about after university? How will you realise your career goals? Develop your transferable skills and experiences to create your personal profile. Reflect on and plan your ongoing personal development, with guidance from your personal advisor within the department. View Research Skills and Employability on our Module Directory COMPONENT 10: CORE WITH OPTIONS MA981-7-FY or MA983-7-SU (60 CREDITS)

---

---

Teaching

• Core components can be combined with optional modules, to enable you to gain either in-depth specialisation or a breadth of understanding
• Learn to use LATEX to produce a document as close as possible to what professional mathematicians produce in terms of organisation, layout and type-setting
• Our postgraduates are encouraged to attend conferences and seminars on a Thursday afternoon

---

---

Assessment

• Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework

---

---

Dissertation

• You will be provided with a list of dissertation titles or topics proposed by staff and it may be possible to propose a project of your own
• Most dissertations are between 10,000 and 30,000 words in length. However, these are guidelines, not mandatory word counts
• Close supervision by academic staff