With the rapid development of smart sensors, smartphones and social media, "big" data is ubiquitous. This new MSc teaches the foundations of GIScience, database, spatial analysis, data mining and analytics to equip professionals with the tools and techniques to analyse, represent and model large and complex spatio-temporal datasets.
Students will be equipped with computational foundations and skills needed for big data analytics including visualisation, prediction, clustering and simulation with statistical and machine learning approaches, as well as retrieving and mining big (open) data, web services and cloud computing, web and mobile applications, by practising with real case data and open software.
Students undertake modules to the value of 180 credits.
The programme consists of four core modules (60 credits), four optional modules (60 credits) and a dissertation/report (60 credits).
A Postgraduate Diploma, four core modules (60 credits), two optional modules (60 credits), full-time nine months is offered.
Core modules
Choose four options from the following:
Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 15,000 words.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, and laboratory practicals. Assessment is through examination, coursework, practicals, dissertation, and poster presentation.
Further information on modules and degree structure is available on the department website: Spatio-temporal Analytics and Big Data Mining MSc
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Graduates from this programme are expected to find positions in consultancy, local government, public industry, and the information supply industry, as well as in continued research. Possible career paths could include: data scientist in the social media, finance, health, telecoms, retail or construction and planning industries; developer of spatial tools and specialised spatial software; researcher or entrepreneur.
Employability
Graduates will be equipped with essential principles and technical skills in managing, modelling, spatial and spatial-temporal analysis, visualising and simulating "big" spatio-temporal data, with emphasis on real development skills including: Java, JavaScript, Python and R. Business Intelligence (BI) skills will also be taught via practical case studies and close collaborations with leading industrial companies and institutions. All these skills are highly valued in big data analysis.
As one of the world’s top universities, UCL excels across the physical and engineering sciences, social sciences and humanities.
Spanning two UCL faculties, this interdisciplinary programme exploits the complementary research interests and teaching programmes of three departments (Civil, Environmental & Geomatic Engineering, Computer Science, and Geography).
Students on the Spatio-Temporal Analytics and Big Data Mining programme will be part of a vibrant, enthusiastic, and international research environment in which collaboration and free-ranging debate are strongly encouraged. This is supported by weekly research seminars and industrial seminars from top employers in the field.
The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.
The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.
The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.
The MSc in Data Science provides you with these skills.
Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.
As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files.
The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice.
The programme includes:
You will study the following core modules:
You will also choose from an anually approved list of modules which may include:
Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government.
The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing.
This could lead to a variety of potential jobs including:
Find out more about employability at Goldsmiths.
Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).
MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.
The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.
The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.
Modules for the MSc Data Science programme include:
- Visual Analytics
- Data Science Research Methods and Seminars
- Big Data and Data Mining
- Big Data and Machine Learning
- Mathematical Skills for Data Scientists
- Data Visualization
- Human Computer Interaction
- High Performance Computing in C/C++
- Graphics Processor Programming
- Computer Vision and Pattern Recognition
- Modelling and Verification Techniques
- Operating Systems and Architectures
The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.
As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.
- Data Analyst
- Data mining Developer
- Machine Learning Developer
- Visual Analytics Developer
- Visualisation Developer
- Visual Computing Software Developer
- Database Developer
- Data Science Researcher
- Computer Vision Developer
- Medical Computing Developer
- Informatics Developer
- Software Engineer
This course was developed and is run in conjunction with SAS, it will provide you with the knowledge and skills to effectively research, develop and apply business intelligence systems. These are computerised information systems which support an organisation in the decision making process. Many of the techniques used in this area are underpinned by predictive statistics and mathematical modelling. This course will emphasise the concepts and techniques of business intelligence systems and their application and development. You will have access to specialist computing laboratories including one suite reserved specifically for postgraduate students. Upon graduating you be well placed to take up more general management and business information systems development roles within industry, or to undertake academic researchin this field.
• Taught by SAS accredited teaching staff
you will be taught by experienced SAS accredited teaching staff providing you with expert knowledge and skills allowing you to work toward your SAS accreditation
• SAS endorsed course
enhance your employability and gain substantial knowledge and skills in SAS business intelligence software leading towards SAS data miner accreditation
• 50 years history of research and teaching in computing technology
benefit from our well established academic expertise and advance your skills in, and knowledge of, developing business intelligence systems and data mining solutions to business problems
• Gain an insight into real world solutions
attend guest lectures and seminars, which will give you a real understanding of the impact of their work
• Excellent graduate prospects
graduates have gone into roles such as BI/SQL developers, logistics data modeller’s and insight analysts at organisations including Cognisco, LLamasoft and Occam DM
First semester
• Fundamentals of Business
Intelligence Systems
• Data Warehouse Design and OLAP
• Research Methods
• Statistics
Second semester
• Data Mining
• Business Intelligence Systems
Application and Development
• Analytics Programming
Plus two from the following list:
• Management of Information Systems
• Human Factors in Systems Design
• Applied Computational Intelligence
• Artificial Neural Networks
Third semester
• Final Project
Teaching will normally be delivered through formal lectures, informal seminars, tutorials, workshops, discussions and e-learning packages. Assessment will usually be carried out through a combination of individual and group work, presentations, reports, projects and exams.
Compulsory taught modules give you the opportunity to gain the fundamental knowledge and practices required to apply, develop and research business intelligence systems, while optional modules provide you with chances to study particular aspects of system application and development in more depth.
The individual project module allows you to undertake research into an aspect of business intelligence systems that interests you, and/or to perform appropriate business intelligence development tasks in response to a given practical problem.
Full-time students will normally attend around 16 hours of timetabled taught sessions per week, and can expect to undertake around 24 further hours of self-directed independent study and research to support your assignments and dissertation.
This course was developed and is run in conjunction with SAS. SAS is the world's largest independent business analytics company. It provides an integrated set of software products and services to more than 45,000 customer sites in 118 countries. Across the globe, both the public and private sector use SAS software to assist in their efforts to compete and excel in a climate of unprecedented economic uncertainty and globalization.
To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx
Applying for a postgraduate course:
Funding for postgraduate students:
There is an enormous and increasing amount of data that is collected. Examples include not just traditional data such as sales transactions, but location data (GPS), interactions between people on social network, measurements of sleep patterns, medication being taken, state of health, and much much more.
A key challenge is then to make use of this wealth of data. How can we manage this data, and analyse it to exploit useful information that can guide decision making?
This emerging area goes under the name “Data Science”. There is growing demand for people, “Data Scientists”, who have the skills to manage and analyse enormous amounts of data using a range of techniques such as data mining, statistical techniques, and machine learning.
Data Scientist has been called the “Sexiest job of the 21st century”, and the unique combination of technical skills (stats, data management) and business understanding has been said to make Data Scientists “highly sought after and highly paid”.
The MBusDataSc primary focus is to equip you to become a practitioner, allowing you to meet the needs of industry, and solve the data problems of the world. However, there will also be an alternative path that will focus on preparing students for research in the area (e.g. going on to do a masters by research or PhD).
The proposed degree is inherently multidisciplinary, featuring Information Science and Marketing, which gives the degree a strong business focus; as well as contributions from Computer Science and from Statistics.
Once you have completed the MBusDataSc you will have developed an advanced knowledge of data science. You will understand how data analysis can be used in business, including being able to identify opportunities to use data, be aware of ethical and privacy issues and possible mitigations, and be able to select appropriate means of presenting the results of analysis. You will be able to select and apply techniques to manage and analyse large collections of data.
The programme of study shall consist of seven 20 point taught papers together with a 40 point applied project or research project. Papers are either taught in semester one, semester two or are full-year papers.
You must complete:
INFO 424 - Adaptive Business Intelligence
COSC 430 - Advanced Database Topics
INFO 411 - Machine Learning and Data Mining
MART 448 - Advanced Business Analytics
INFO 420 - Statistical Techniques for Data Science
INFO 408 - Management of large scale data
BSNS 401 - The Environment of Business & Economics
Plus one of the following project papers
or
BSNS 580 - Research Project (for students who may wish to progress to PhD study)
The University of Otago coursework masters programmes provide you with an opportunity to specialise in advanced study with a focus on either applied practical or academic research.
Graduates of the MBusDataSc will gain skills in three areas: those relating to the business and organisational context, those relating to computing technologies for managing data, and those relating to data analysis techniques.
As a graduate of the MBusDataSc you should be able to:
Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?
This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to a lack of suitably qualified people. This course is unique in the UK in that it has been developed as a MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem solving, and an ambition for a career in data science; then this course is for you.
During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite due to the partnership of the University with the SAS Student Academy.
"We are especially pleased to endorse the new MSc in Data Science. With the explosion of interest and investment in data science teams, our customers cannot get enough graduates with SAS-based analytical skills. Courses such as this new MSc are an important step forward by the University to addressing this skills shortage, especially amongst home students." - SAS
This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project worth 60 credits. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.
The course is focused around the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for examination. This makes a total of 300 hours total learning experience.
A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”
With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.
The Informatics Research Centre in the School of Computing, Science and Engineering at the University of Salford builds on the history, success and achievements of the research in Computer Science and Information Systems developed at the University of Salford over the last thirty years.
Evolving around Data and Information in all their types and usages, the Centre covers all phases and processes from data pre-processing to engineering and visualisation. The Centre is developing novel methods and systems for the analysis and recognition of various data sets, learning behaviours and causal models. The techniques and systems developed have a wide range of potential applications including digitisation of historical documents, medical diagnosis, semantic tagging, segmentation of types of viewers and their behaviours, text mining and retrieval and data visualisation.
Forensic computing, digital investigation and Cyber security is another area of expertise supported by the centre both at the theoretical and application levels.
Many students go on to further research in the fields of:
Facilities include a new Dell Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialized in Machine Learning, Data Mining, Statistical Analysis and Big Data including:
Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs.
This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.
The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management.
In the statistics section you study modules on data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.
The computing section covers areas related to data integration, massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes.
The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.
Our partnerships with business inform the course design, ensuring the content is relevant, up to date and meets the needs of industry. These partnerships also enable the inclusion of some leading edge software such as SAS, SAP Hana, and Hadroop within the course. You may be able to study abroad as part of the Erasmus programme.
Key areas of study
Key areas of study include • data quality and analysis • technologies to store and mine data • professionalism and research
Professional recognition
This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam.
Sheffield Hallam is a member of the SAS Student Academy, the SAP Student Academy and founding member of the SAP University Alliance.
Full time – September start – typically 12 or 18 months
Part time – September start – typically 36 months
Core modules
Options
Choose one from :
Many jobs for data scientists, data analysts and data mining analysts are available with salaries ranging from £35,000 to £80,000.
Jobs typically list the skills to be in areas such as statistical analysis and machine learning techniques, database and programming technologies, and expertise in statistical theory, which are all areas you cover on this course.
You also gain skills and knowledge in HaDoop, MapReduce, Java, SAS, MSQL which are some of the common technologies used in data scientist roles.
Data Analytics MSc has been developed in collaboration with SAS; a world leader in data analytics. Due to this strong partnership you will gain substantial SAS software skills as part of your study. This course is designed specifically to equip you with the skills and abilities to address the skills shortage in industry. On successful completion of the course you will have developed your analytic and technical knowledge, and enhanced your professional skills within a Business Intelligence context. Upon graduating you will be prepared to undertake business intelligence and data mining roles within any target industry
• Taught by SAS accredited teaching staff
you will be taught by experienced SAS accredited teaching staff providing you with expert knowledge and skills
• Developed to fill skills shortage
course content has been developed to enhance your employability and gain substantial knowledge and equipping you with the skills required in for the use of the SAS software as well as Hadoop Distributed File System (HDFS) in industry
• 50 years history of research and teaching in computing technology
benefit from our well established academic expertise and advance your skills in, and knowledge of, data analytics to business problems
• Industry placement opportunity
you can chose to undertake a year-long work placement gaining valuable experience and skills as well as networking opportunities to build your industry contacts
• Excellent graduate prospects
equipped with the relevant skills for business intelligence and data mining roles including SAS Programming, Database Design and Business Intelligence
First semester
• Statistics
• Fundamentals of Business Intelligence Systems
• Research Methods
• Data Warehouse Design and OLAP
Second semester
• Analytics Programming
• Business Intelligence Systems
Application and Development
• Big Data Analytics
• Data Mining
Third semester
• Individual project
Teaching will normally be delivered through formal lectures, informal seminars, tutorials, workshops, discussions and e-learning packages. Assessment will usually be carried out through a combination of individual and group work, presentations, reports, projects and exams.
The course is run in association with SAS, the leading independent vendor in the business intelligence industry, and you will gain substantial SAS software skills as part of your study.
First semester modules provide you with fundamental abilities in the use of statistics so that you can gain insights and practice of using business intelligence systems and analytics programming to exploit multidimensional data sets.
In the second semester you are exposed to a variety of business intelligence systems, including those that use big data and data mining techniques. A further module prepares students to undertake an individual research project. This project module allows you to undertake extensive research into an aspect of big data, and/or provides an opportunity to develop and demonstrate your analytical and processing abilities in response to a given practical problem.
You will normally attend 3 hours of timetabled taught sessions each week for each module undertaken during term time, for full time study this would be 12 hours per week during term time. You are expected to undertake around 24 further hours of independent study and assignments as required per week.
The Data Analytics MSc was developed and is run in conjunction with SAS. SAS is the world's largest independent business analytics company. It provides an integrated set of software products and services to more than 45,000 customer sites in 118 countries. Across the globe, both the public and private sector use SAS software to assist in their efforts to compete and excel in a climate of unprecedented economic uncertainty and globalization.
To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx
Applying for a postgraduate course:
Funding for postgraduate students:
This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.
You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.
Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools and methods. These include warehousing and data mining, distributed data management, and the technologies, architectures, and appropriate middleware and infrastructures supporting application layers. The second theme will enhance your knowledge of algorithms and the quantitative techniques suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area.
Teaching approaches include lectures, tutorials, seminars and practical sessions. You will also learn through extensive course work, class presentations, group research work, and the use of a range of industry standard software such as R, Python, Simul8, Palisade Decision Tools, Hadoop and Oracle.
Taught modules may be assessed entirely through course work, or may include a two-hour exam at the end of the year.
The following modules are indicative of what you will study on this course.
Core modules
Option modules
This programme is accredited by BCS, The Chartered Institute for IT, for fully meeting the further learning educational requirement for Chartered IT Professional (CITP) status and for partially satisfying the underpinning knowledge requirements set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC) and the Science Council for Chartered or Incorporated Engineer (CEng or IEng) status. Note that there are additional requirements, including work experience, to achieve full CITP, CEng, or IEng status. Graduates of this accredited degree will also be eligible for professional membership of BCS (MBCS).
The BCS accreditation is an indicator of the programme’s quality to students and employers; it is also an important benchmark of the programme’s standard in providing high quality computing education, and commitment to developing future IT professionals that have the potential to achieve Chartered status. The programme is also likely to be recognised by other countries that are signatories to international accords.
Graduates can expect to find employment as consultants, decision modelling or advanced data analysts, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (e.g. GlaxoSmithKline, British Airways, Santander and Unilever), public sector organisations (e.g. the NHS and primary care trusts), retail head offices, the BBC, the Civil Service, and the host of banks, brokers and regulators that make up the City, along with all the specialist support consultancies in IT and market research and forecasting, all of whom use data for the full range of decision making.
Our Work Placement Teams are based in your Faculty Registry Office and can help you find a suitable placement, as well as support you in making applications, writing CVs and improving your interview technique.
More details on work placements can be found on our Work placements page.
This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.
You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.
Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools and methods. These include warehousing and data mining, distributed data management, and the technologies, architectures, and appropriate middleware and infrastructures supporting application layers. The second theme will enhance your knowledge of algorithms and the quantitative techniques suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area.
Teaching approaches include lectures, tutorials, seminars and practical sessions. You will also learn through extensive course work, class presentations, group research work, and the use of a range of industry standard software such as R, Python, Simul8, Palisade Decision Tools, Hadoop and Oracle.
Taught modules may be assessed entirely through course work, or may include a two-hour exam at the end of the year.
The following modules are indicative of what you will study on this course.
Core modules
Option modules
This programme is accredited by BCS, The Chartered Institute for IT, for fully meeting the further learning educational requirement for Chartered IT Professional (CITP) status and for partially satisfying the underpinning knowledge requirements set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC) and the Science Council for Chartered or Incorporated Engineer (CEng or IEng) status. Note that there are additional requirements, including work experience, to achieve full CITP, CEng, or IEng status. Graduates of this accredited degree will also be eligible for professional membership of BCS (MBCS).
The BCS accreditation is an indicator of the programme’s quality to students and employers; it is also an important benchmark of the programme’s standard in providing high quality computing education, and commitment to developing future IT professionals that have the potential to achieve Chartered status. The programme is also likely to be recognised by other countries that are signatories to international accords.
Graduates can expect to find employment as consultants, decision modelling or advanced data analysts, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (e.g. GlaxoSmithKline, British Airways, Santander and Unilever), public sector organisations (e.g. the NHS and primary care trusts), retail head offices, the BBC, the Civil Service, and the host of banks, brokers and regulators that make up the City, along with all the specialist support consultancies in IT and market research and forecasting, all of whom use data for the full range of decision making.
Our Work Placement Teams are based in your Faculty Registry Office and can help you find a suitable placement, as well as support you in making applications, writing CVs and improving your interview technique.
More details on work placements can be found on our Work placements page.
Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries.
Designed in close consultation with industry partners including the NHS Business Services Authority, Teradata, BT, SAS, the Pensions Regulator and local Brighton companies, your learning is informed by current business developments through case studies looking at real-world data sets, research questions and scenarios. You have the opportunity to collaborate on projects with our industry partners, and can also use your own data, project ideas and industry links.
Guest lecturers will share their knowledge and expertise with you, such as Tom Khabaza who is a founding chairman of the Society of Data Miners, author of 9 Laws of Data Mining and was involved in designing the course.
You will develop a skill set in specialist data analytics and associated software, quantitative methods and techniques, and business intelligence. Our staff are experts in their field and you have the chance to develop your knowledge in specialist areas where we have ongoing research and expertise, such as sequential forecasting, natural language processing and image processing.
Whether you are a recent graduate or an experienced professional wanting to gain data analysis skills, this course is available on a full or part-time basis to help you manage your studies around other commitments.
The course covers three main areas:
You will learn how to assess project viability, propose sound business cases and strategies for analysis, perform and oversee analysis and manage large data projects successfully as well as developing your critical appraisal and presenting techniques.
Based at our Moulsecoomb campus, you will have access to computer and research labs equipped with specialist, sophisticated software including SAS, SPSS Statistics and SPSS Modeller. Affordable student licences for home use are also available.
With a flexible timetable to suit full-time or part-time students and commuters, and lecturers available to support you in your module choices, there are different study routes available to you.
You will study five core modules. One of these involves a major project, potentially in collaboration with industry. You will also choose option modules, subject to availability, allowing you to focus on particular areas of interest.
Core modules
Option modules*
*Option modules are indicative and may change, depending on timetabling and staff availability.
A wide variety of organisations draw upon data analytics specialists to help produce valuable information for decision-making, for example commodity price forecasting, customer intelligence, clinical trials, R&D and many other areas utilising large amounts of data.
Graduates are able to choose from a range of private, governmental and academic roles, depending on their personal interests. Some of our full-time students find a full-time job and switch to part-time study in the middle of the course.
Graduate destinations include:
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