Data Science and Computational Intelligence MSc 2020/21 entry

Course code:

EECT044

Study options:

1 year full-time

 

Location:

Coventry University

Starting:

September 2020

 

Fees:
Faculty:

Get in touch

For questions regarding study and admissions please contact us:

+44 (0) 24 7765 4321
ukpostgraduate@coventry.ac.uk

Overview

This Master’s course aims to respond to the demand for data scientists with the skills to develop innovative computational intelligence applications, capable of analysing large amounts of complex data to inform businesses decisions and market strategies.

Multidisciplinary content covers machine learning, neural networks, big data analysis, information retrieval, fuzzy systems and evolutionary computation. You will have opportunities to undertake practical projects, applying your learning to real-life problems in business, finance, security, industry control, engineering, natural language processing, information retrieval and bioinformatics.

Coventry University offers chances to learn alongside active researchers in areas such as pervasive computing, distributed computing and application, as well as innovative applications for interactive virtual worlds.

Work placement option

This master's programme provides you with the additional option to apply for a 'work placement' opportunity during your first semester with us. The 'work placement' is designed to further develop your skills, knowledge and professional experience with the aim of maximising your employability prospects. Please note that the optional placement modules incur an additional tuition fee of £4,000. Find out more about the work placement pathway.

Why Coventry University?

An award-winning university, we are committed to providing our students with the best possible experience. We continue to invest in both our facilities and our innovative approach to education. Our students benefit from industry-relevant teaching, and resources and support designed to help them succeed. These range from our modern library and computing facilities to dedicated careers advice and our impressive Students’ Union activities.

Global ready

An international outlook, with global opportunities

Employability

Career-ready graduates, with the skills to succeed

Student experience

All the support you need, in a top student city

What our students say...

At first I was apprehensive on starting this Master's programme due to my Mechanical Engineering background. However, the professors and staff provided all the support and guidance I needed to succeed in the modules. I feel much more confident to continue my career in this area since the top required skills were covered in the program. An online data science course could not have provided the same level of personal experience and quality.

Juliana Negrini De Araujo, MSc Data Science and Computational Intelligence, graduated 2019

Course information

Big data processing and information retrieval drives some of the world most successful and high-tech businesses; from well-known companies like Google and Twitter, to specialist medical informatics providers and even space exploration.

The main theme throughout this course is automatic big data processing and information retrieval through machine learning, neural network and evolutionary computing. We will cover how to apply cutting-edge machine learning techniques to analyse big datasets, assess the statistical significance of data mining results and perform advanced data mining tasks.

We will introduce you to important frameworks which may include Hadoop Map Reduce, Spark, applications of relational databases and NoSQL databases in combination with easy to use and powerful development tools such as Scala, Python, Matlab and R. We will look at emerging theories, practices, approaches and management of distributed and intelligent computing systems, examining a wide range of case studies to see how applications have been developed and for what purposes, such as steganography detection system for colour stego images.

The focus of the MSc Data Science and Computational Intelligence course is on applications of data science methods and tools, combined with computational intelligence techniques for data-driven problem solving including the analysis, interpretation and visualisation of complex data, which is in increasing demand in fields such as marketing, pharmaceutics, finance, transportation, medicine, and management.

A unique aspect of this course is the delivery through a wide range of activities and problem based learning in the context of the current research or industry consultancy projects, conducted by the academics responsible for teaching on this course.

Course Specification
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Modules

Overview

The course is composed of a combination of modules which progressively build to the award of a Postgraduate Certificate (PgCert), Postgraduate Diploma (PgDip) or Master’s (MSc). You can choose one of three specialist routes in Data Science and Computational Intelligence, all of which aim to advance your knowledge in this field.

In the first semester, you will study four mandatory modules: Introduction to Statistical Methods for Data Science, Machine Learning, Data Management Systems and Intelligent Information Retrieval. These modules aim to familiarise you with the theoretical and practical aspects of the machine learning and artificial intelligent techniques which are being widely used in data analysis and problem solving of real world applications.

In the second semester, you will have the opportunity to learn the latest techniques of machine learning and computational intelligence in the Advanced Machine Learning module. Furthermore, you will be familiarised with the theoretical and practical aspects of advanced database systems, big data management and big data visualisation. In this semester, you will also learn to develop the research skills required for advanced data science and computational intelligence topics, selected according to your interests.

In the final semester, you will be expected to apply the knowledge and skills you have learned in the first two semesters by undertaking an in-depth individual project which may be industry-based or undertaken in collaboration with one of the university research groups. Guided by an expert tutor, this project helps to develop your research and practical skills while also gaining professional data science experience.

Past student projects have included: analysing big data for the movie industry; helping in assessing oil wells to evaluate extraction costs; recognising customer emotion through face gestures; analysing text sentiment in a social media exchange; performing automatic scene labelling and object recognition; and designing a robotics controller for helping the elderly.

Past student projects have included: analysing big data for the movie industry; helping in assessing oil wells to evaluate extraction costs; recognising customer emotion through face gestures; analysing text sentiment in a social media exchange; performing automatic scene labelling and object recognition; and designing a robotics controller for helping the elderly.

This course includes the Consulting Global Professional Development module developed in partnership with the Chartered Management Institute.

Without work placement pathway

Semester One
  • Machine Learning
  • Data Management Systems
  • Introduction to Statistical Methods for Data Science
  • Intelligent Information Retrieval
Semester Two
  • Big Data Management and Data Visualisation
  • Artificial Neural Networks
  • Advanced Machine Learning
  • Individual Research Project Preparation
Semester Three
  • Global Professional Development – Consultancy
  • Computing Individual Research Project

‘With work placement’ pathway

The ‘With work placement’ opportunity enables you to apply in semester 1 for an optional work placement of up to 12 months, extending the duration of your Masters to 24 months. The placement provides an opportunity for you to develop expertise and experience in your chosen field with the aim of enhancing your employability upon graduation.

Please note that the optional placement modules incur an additional tuition fee of £4,000. Placement opportunities may also be subject to additional costs, visa requirements being met, subject to availability and/or competitive application. Work placements are not guaranteed but you will benefit from the support of our employability and placements team EEC Futures in trying to find and secure an opportunity.

Semester One
  • Machine Learning
  • Data Management Systems
  • Introduction to Statistical Methods for Data Science
  • Intelligent Information Retrieval
Semester Two
  • Big Data Management and Data Visualisation
  • Artificial Neural Networks
  • Advanced Machine Learning
  • Individual Research Project Preparation
Semester 3
  • Work placement (Part One)
Semester 4
  • Work placement (Part Two)
Semester 5
  • Work placement (Part Three)
Semester 6
  • Global Professional Development – Consultancy
  • Computing Individual Research Project

We regularly review our course content, to make it relevant and current for the benefit of our students. For these reasons, course modules may be updated.

In more detail...

  • 90% overall student satisfaction for the teaching of computing, above the sector average of 80%, in the Postgraduate Taught Experience Survey (PTES) 2016.
  • 42% of ‘Computer Science and Informatics’ research was judged to be world leading or internationally excellent in the Research Excellence Framework (REF 2014).
  • An Engineering and Computing building that features a large range of specialised computing laboratories in computer security, communications and signal processing, electrical, electronics and microprocessors, ethical hacking and forensic computing, together with a games and multimedia studio and open access computer facilities.

Your main study themes are:

  • Introduction to Statistical Methods for Data Science: This module aims to provide you with knowledge of widely used statistical methods, and their application, in data science. On successful completion of this module, you should have learned the fundamental principles of probability theory and statistics, including distribution theory, required on the course. In addition, this module introduces distribution theory and statistical inference, including estimation, maximum likelihood estimators, hypothesis testing, and the foundation of Bayesian inference. This module then introduces important general aspects of statistical modelling and data analysing for computer and simulation experiments. The general principle of a wide range of well-known statistical models will be studied in detail and their usage to solve some real world applications will be demonstrated using the modern statistical software.
  • Intelligent Information Retrieval: Information retrieval is among the core activities driving some of the world’s successful and high-tech businesses including Google, Facebook, and Twitter. This module aims to expose you to a range of common information retrieval methods, including both theory and practice. The module mainly emphasises text retrieval, covering only a brief outline of multimedia information retrieval. In addition, it emphasises data mining methods, especially for text classification and document clustering problems. Course work requires implementation in a computer programming language.
  • Data Management Systems: This module aims to provide you with a sound knowledge of the theoretical and practical underpinnings of data management systems in centralised and distributed environments. This module is motivated by the need to harness the potential of traditional systems and of modern schemes, which arose in response to the challenges posed by big data. The organisation and delivery of the module implements a two-pronged approach, through the investigation of data models and through the study and application of relational databases and NoSQL databases. The study of distributed frameworks such as Hadoop, and relevant distributed techniques such as sharding and replication, will provide further enhancement to the scope of the module.
  • Machine Learning and Data Mining: Machine learning is the process whereby systems learn by identifying structures and patterns within data. As such, it has proved an important tool in various applications, including data mining, games design, diagnosis and natural language processing. We cover a broad field of intelligent techniques and their associated algorithms, such as data preparation and pre-processing, reinforcement learning, supervised and unsupervised learning, classification and clustering, applications of machine learning and future developments.
  • Artificial Neural Networks: This module introduces the concepts used in neural networks and their application to solving real-world problems. Neural networks, a popular machine learning approach that attempts to model how the human brain works, is used in a wide range of applications, including image processing, speech and natural language processing, medical diagnosis, bioinformatics and computational biology, emotion recognition, robotics and control. We will explore different neural network computational approaches and structure, neural network learning and teaching, convolutional neural networks, deep learning and applications of neural networks in plenty of industrial settings.
  • Big Data Management and Data Visualisation: Organisations and businesses are being inundated with very large volumes of data - structured and unstructured - on a daily basis. This data is too big and complex for processing and analysing using well known traditional methods. This module aims to introduce you to the current management and visualisation methods for big data. Cutting edge techniques will be taught which will enable you to discover patterns, relationships and associations in big data sets. You will have the opportunity to engage with the emerging critical issues within the context of traditional database management systems which make them unsuitable to process big data. Thus, the nature of big data, recognised by its volume, velocity and variety, which prevents analysis in the normal setting of a traditional database, will be studied and advanced analytical techniques required to understand big data will be covered.
  • Advanced Machine Learning: This module aims to provide you with an understanding of more advanced concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module is structured around recent developments on: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and topic modelling, and probabilistic graphical models as a powerful framework for representing complex domains using probability distributions. This module will also introduce evolutionary algorithms and fuzzy systems from an application oriented standpoint. We study the key concepts of fuzzy sets as a methodology for handling imprecise and uncertain information. Applications of fuzzy systems and fuzzy controllers will be discussed along with coverage of their use in hybrid intelligent systems in combination with other Computational Intelligence (CI) techniques.
  • Individual Research Project Preparation: This module provides the background in study skills and research methods needed to enable you to carry out an applied or enquiry-based research project.
  • Master’s Project: You will undertake an overarching project under the supervision of one of our highly qualified academics. The project will cover several aspects of the studied techniques and implementation from the taught modules. Guided by an expert tutor, this project helps to develop your research and practical skills and gain experience similar to that of a data scientist professional. Previous student projects have included: analysing big data for the movie industry; oil extraction and refinement analysis; customers’ emotion recognition; speech and tone recognition; text sentiment analysis; performing automatic scene labelling and object recognition; fuzzy controllers; and designing a neural networks robotics controller for helping the elderly.

The programme allows you to study full-time over one year or part-time over two years, starting in September.

We stress practicality whenever appropriate and try to strike a good balance between theory and application, as well as industrial-related experience and current research topics. A variety of equipment and software are used for these purposes, including HPC clusters and a GPU server purposely built for deep machine learning and neural networks tasks.

There may be opportunities to attend external talks, by our academic and research staff, as well as visiting lecturers, which aim to bring you the latest issues on a wide range of topics, such as intelligent services, image processing and big data analytics.

Teaching methods include: a range of creatively designed practical tasks, projects, lectures and tutorials.

This course will be assessed using a variety of methods which will could vary depending upon the module. Assessment methods include coursework, essays, project, group work and formal examination.

The Coventry University assessment strategy ensures that our courses are fairly assessed and allows us to monitor student progression towards the achieving the intended learning outcomes. Assessments may include exams, individual assignments or group work elements.

On successful completion, you should have knowledge of:

  • The fundamental principles and techniques of data science and computational intelligence.
  • Analysing complex, high-volume, high-dimensional, structured/unstructured data from varying sources.
  • The combination of theory and practical application of data science and computational intelligence methods and techniques.
  • Professional, legal, social, cultural and ethical issues related to data science, computational intelligence and an awareness of societal and environmental impact.

On successful completion, you should be able to:

  • Critically evaluate current research problems and apply cutting-edge developments of data science and to computational intelligence areas.
  • Critically evaluate a range of possible options solutions or architectures to address a sizeable data application and present a soundly reasoned justification for the final solution.
  • Demonstrate competence, creativity and innovation in solving unfamiliar problems.
  • Communicate effectively outcomes from major projects to technical and non-technical audiences. Select and apply relevant knowledge and skills in big data applications using relevant tools and technologies.
  • Identify and make effective and systematic use of a range of suitable techniques for developing solutions to complex data and analytical problems.

In a typical teaching week, you will have up to 12 ‘contact’ hours of teaching. There are four modules in the first semester, four in the second and you will spend the third semester on your project. Teaching of each module comprises around three hours of ‘contact’ time, which consists of four hours of lectures each week and around eight hours of practical laboratory classes each week.

In addition, you will be expected to undertake a further 28 hours of self-directed study each week eg. some guided study using handouts, and online activities.

If you have a desire to travel, it is possible to spend a period abroad for part of your studies, for as little as two weeks. We also offer you the chance to participate in field trips to a number of different overseas locations, which have previously included China, Poland, Spain and Finland.

Please note that such field trips may be subject to additional costs, competitive application, meeting applicable visa requirements and availability.

Global ready

Did you know we help more students travel internationally than any other UK university according to data from the experts in higher education data and analysis, HESA?

In 2016/17, we were able to provide a total of 3,482 student experiences abroad that lasted at least five.

Much of this travel is made possible through our Global Leaders Programme, which enables students to prepare for the challenges of the global employment market, as well as strengthening and developing their broader personal and professional skills.

Explore our international experiences

1st for

international experiences

Sending more students overseas than any other UK uni (HESA 2016/17)


3,482

Student experiences

The number of student trips abroad for at least 5 days in 2016/17


21,000

and counting

The number of students we’ve helped travel internationally so far

12

global programmes

As well as trips, we offer other opportunities like language courses


What our students say...

At first I was apprehensive on starting this Master's programme due to my mechanical engineering background. However, the professors and staff provided all the support and guidance I needed to succeed in the modules. I feel much more confident to continue my career in this area since the top required skills were covered in the program. An online data science course could not have provided the same level of personal experience and quality.

Juliana Negrini De Araujo, MSc Data Science and Computational Intelligence, graduated 2019

Entry Requirements

Applicants for this programme will normally be expected to possess a minimum of a second class honours degree in Computer science, Mathematics or other relevant area.

Applicants for this programme will normally be expected to possess a minimum of a second class honours degree in Computer science, Mathematics or other relevant area.

English as a Foreign Language: This course requires IELTS of 6.5 overall, with no component lower than 5.5. Alternatively, students may be admitted with IELTS 6.0 subject to successfully completing a compulsory five week pre-sessional English course.

Our International Student Hub offers information on entry requirements for your country, as well as contact details for agents and representatives should you need more advice.

More detail

Applicants for this programme will normally be expected to possess a minimum of a second class honours degree in Computer science, Mathematics or other relevant area.

English as a Foreign Language: This course requires IELTS of 6.5 overall, with no component lower than 5.5. Alternatively, students may be admitted with IELTS 6.0 subject to successfully completing a compulsory five week pre-sessional English course.

Our International Student Hub offers information on entry requirements for your country, as well as contact details for agents and representatives should you need more advice.

More detail

Tuition Fees

We pride ourselves on offering competitive tuition fees which we review on an annual basis and offer a wide range of scholarships to support students with their studies. Course fees are calculated on the basis of what it costs to teach each course and we aim for total financial transparency.

Starts

Fee


September 2020

TBC (per year)


UK Scholarships

If you're a truly outstanding undergraduate candidate we may be able to offer you a Coventry University Scholarship. Coventry University Scholarships are awarded to recognise truly exceptional sports achievement and academic excellence.

Starts

Fee


September 2020

TBC (per year)


EU Scholarships

We're investing into scholarships for high achieving and enterprising students.

Our scholarships are worth up to £10,000 and every student that applies will be considered. Fulfil your potential this academic year with Coventry University!

Starts

Fee


September 2020

TBC (per year)


International Scholarships

We're investing into scholarships for high achieving and enterprising students.

Our scholarships are worth up to £10,000 and every student that applies will be considered. Fulfil your potential this academic year with Coventry University!

Course essentials – additional costs

Subject to a successful application for the optional work placement, a £4000 placement fee is payable in semester two.

EU student fees

EU nationals and their family members starting in the 2019/20 academic year remain eligible for the same fees as home students and the same financial support. Financial support comes from Student Finance England, and covers undergraduate and postgraduate study for the duration of their course, providing they meet the residency requirement.

For tuition fee loans

EU nationals must have resided in the European Economic Area (EEA) or Switzerland for the three years prior to the start of their course. The purpose of that three year residency should not have been mainly for the purpose of receiving full time education.

For maintenance loans

EU nationals must have resided in the UK and Islands for the five years prior to the start of their course. The purpose of that five year residency should not have been mainly for the purpose of receiving full time education.

What our students say...

At first I was apprehensive on starting this Master's programme due to my mechanical engineering background. However, the professors and staff provided all the support and guidance I needed to succeed in the modules. I feel much more confident to continue my career in this area since the top required skills were covered in the program. An online data science course could not have provided the same level of personal experience and quality.

Juliana Negrini De Araujo, MSc Data Science and Computational Intelligence, graduated 2019

Career prospects

The highly-regarded Harvard Business Review has previously declared the role of ‘data scientist’ to be the sexiest career of the 21st century.

The explosion in the amount of available data now available to businesses, coupled with its potential value to improve competitive advantage, is driving the continued strong demand for data scientists throughout the world.

A 2014 survey by consulting firm Accenture on clients’ big-data strategies in April 2014 revealed that more than 90% planned to hire more employees with expertise in data science, yet almost half cited a lack of talent as a chief obstacle.

Opportunities following successful completion of this course could include careers as data scientists, data professionals and data analysts in variety of sectors including financial services, retail, marketing, customer and business intelligence.

The practical nature of our course places an emphasis on your future employability, developing a wide range of technical, analytical, design and professional skills.

Our faculty’s employability and placements unit EEC Futures, which won the ‘Best Placement Service in the UK’ award at the National Undergraduate Employability Awards in 2015 and 2016, will help you in finding work experience while you study and employment on graduation. Opportunities also exist to complete a PhD research degree upon completion of the course.

Coventry University is committed to preparing you for your future career and giving you a competitive edge in the graduate job market. The University’s Careers and Employability team provide a wide range of support services to help you plan and prepare for your career.

Where our graduates work

This master’s course aims to provide students with the analytical tools to construct more desirable technical solutions using advanced computational methods, with an emphasis on rigorous statistical reasoning. As a result, graduates should gain the skills for roles in a wide range of sectors including; finance, marketing, academia, scientific research, health and medicine, the retail market, information technology, government, ecommerce, energy, transportation, telecommunications, biotechnology and pharmaceutical companies.

Disclaimer

By accepting your offer of a place and enrolling with us, a Student Contract will be formed between you and the University. A copy of the 19/20 Contract can be found here. The Contract details your rights and the obligations you will be bound by during your time as a student and contains the obligations that the University will owe to you. You should read the Contract before you accept an offer of a place and before you enrol at the University.

The tuition fee for the course that is stated on the webpage and in the prospectus will apply. If the duration of the course is longer than one academic year, the University may increase the fee for each subsequent year of study but any such increases will be no more than inflation.

*2020/2021 course fees have not yet been determined by the University or government legislation. Once the course fees have been determined, the course web page will be updated to reflect your first year course fee. Please continue to revisit the course web page to identify the fee before you accept an offer of a place on the course and/or before you enrol at the University.

Coventry University ranked 15 in the UK
Coventry University awarded TEF GOLD Teaching Excellence Framework
University of the year for student experience
QS Five Star Rating 2019
Coventry City of Culture 2021