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Masters Degrees (Visual Analytics)

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Visual analytics is a key requirement of the early 21st century. As our human activity generates rapidly increasing amounts of new data every day, there is an urgent need to make sense of it and a huge potential to elicit new knowledge and insights from it. Read more
Visual analytics is a key requirement of the early 21st century. As our human activity generates rapidly increasing amounts of new data every day, there is an urgent need to make sense of it and a huge potential to elicit new knowledge and insights from it.

World events and phenomena like climate change, 9/11, global finance systems and public health are data rich and increasingly complex. Visual analytics turns large and complex, data sets into interactive visualisations that can prompt visceral comprehension and moments of insight that are compelling and offer an unparalleled richness of possibility for data analysts. In this uncharted world of boundless data, visual analytics is providing our new maps and new ways of navigating. Data analytics is recognised as a key trend that will have a major impact on the IT and Communications industry in the next 5 years

Middlesex University is the recognised centre for excellence in visual analytics the UK and leads the UK Visual Analytics Consortium (UKVAC). This group is working at the leading edge of Visual Analytics and works on a global basis. The UK partners are: Imperial College, University College London (UCL), Swansea, Bangor and Oxford Universities.

We are growing our community of practice in visual analytics and this is a chance to join our team, to work alongside some of the best UK researchers and to get involved in leading edge work. The course will build your professional network, your understanding and your portfolio of experience. Visual Analytics is an interdisciplinary, creative and technical activity. That's why we've built a masters course that is unique. You'll be working for most of the time on your own research and development project, supported by great supervisors, and connecting with new people, theories and practices through workshops that are spread throughout the year. You'll leave with unique insights, strong skills and great contacts; you'll also pick up an MSc along the way.

As a student of this course you'll receive a free electronic textbook for every module.

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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

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.

Key Features of the MSc Data Science

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

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

Facilities

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.

Career Destinations

- 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

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Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights. Read more
Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights.

Who is it for?

This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.

Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.

Objectives

The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.

City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.

The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.

Placements

There is the opportunity to do an internship as part of the programme. The final project, which is normally three months for a full-time student, can be extended to six months if you want to study within a specific organisation. When it comes to the big data and data science area, we have established relationships with organisations including the BBC, Microsoft and The British Library so you can be confident that with City, your access to professional experience is unparalleled. One recent student undertook an internship with Google and has since secured a job within the company.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, you will have access to an array of tools to support your learning.

The MSc Data Science programme offers two (three by mid 2016) dedicated computer servers for the Big Data module, which you can also use for your final project to analyse large data sets. We give you the opportunity to undertake training in MATLAB, the most popular numerical and technical programming environment, while you study.

Scholarships

A scholarship for the full fees of the MSc will be offered to an outstanding applicant. The scholarship is available to UK/EU and overseas students, studying full-time. To be considered for the scholarship, please include with your full application a one-page essay with your answer to the question:

'What are the challenges that Data Science faces and how would you address those challenges?'

The submission deadline for anyone wishing to be considered for the scholarship is: 1 MAY 2017

Teaching and learning

The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:
-Present and exemplify the concepts underpinning a particular subject.
-Highlight the most significant aspects of the syllabus.
-Indicate additional topics and resources for private study.

Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.

Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.

Course content

Data science is the area of study concerned with the extraction of insight from large collections of data.

The course covers the study, integration and application of advanced methods and techniques from:
-Data analysis and machine learning
-Data visualisation and visual analytics
-High-performance, parallel and distributed computing
-Knowledge representation and reasoning
-Neural computation
-Signal processing
-Data management and information retrieval.

It gives you the opportunity to specialise so, once you graduate, you can apply data science to any sector from health to retail. By engaging with researchers and industrial partners during the programme, you can develop your knowledge and skills within a real-world context in each of the above areas.

Core modules
-Principles of data science (15 credits)
-Machine learning (15 credits)
-Big Data (15 credits)
-Neural computing (15 credits)
-Visual analytics (15 credits)
-Research methods and professional issues (15 credits)

Elective modules
-Advanced programming: concurrency (15 credits)
-Readings in computer science (15 credits)
-Advanced databases (15 credits)
-Information retrieval (15 credits)
-Data visualisation (15 credits)
-Digital signal processing and audio programming (15 credits)
-Cloud computing (15 credits)
-Computer vision (15 credits)
-Software agents (15 credits)

Individual project - (60 credits)

Career prospects

From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.

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Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. Read more
Centennial College's Marketing Research and Analytics program positions you at the forefront of a cutting edge job market in which organizations have oceans of data available to them but struggle to make sense of it as marketing becomes increasingly data-driven. As a result, there is a large and growing demand for trained researchers who can harness the power of big data using the latest tools and analytical techniques to uncover new insights and drive businesses forward.

This Marketing Research and Analytics program combines advanced courses in marketing research and big data analytics with training on leading commercial technologies and platforms and the opportunity to gain in-demand industry certifications.

This program equips you with knowledge, skills and training in leading business intelligence and marketing research technologies and tools used in the field. Among them are SAS Enterprise Guide and SAS Enterprise Miner, Environics Analytics Envision (used to develop comprehensive profiles of selected target markets), SPSS, Tableau (the leading data visualization software), Excel, XL Miner, Dell Factiva and NVIVO (qualitative research and text analysis software).

Upon graduation, you receive an Ontario Graduate Certificate from Centennial College, plus certificates of recognition from SAS and Environics Canada. In addition, you are put on an accelerated track to earning the Certified Marketing Research Professional (CMRP) designation, the premier credential in Canadian marketing research from the Marketing Research and Intelligence Association (MRIA).

Career Opportunities

Program Highlights
-The Marketing – Research and Analytics program combines marketing research principles and skills with cutting edge "big data" analytics techniques to equip you with the training required to deliver insights and strategies to help organizations make smarter and more impactful business decisions.
-Employed is an extensive use of learner-centered approaches such as case studies, simulations and project-based learning, with a focus on developing project management, teamwork, analytical thinking, and report writing and presentation skills.
-Hands-on learning covers areas such as questionnaire design, data manipulation, quality control, statistical output and program development.
-There is a strong focus on applying marketing research and analytics to strategic marketing decision-making.
-In the second semester, you develop and implement a capstone project that will integrate and apply your learning.
-In addition to market research technologies, you also have access to the full suite of Microsoft products, including Microsoft Excel, XL Miner, Access and PowerPoint.
-Once you graduate, you have the option to take the Comprehensive Marketing Research Exam (CMRE) on campus at Centennial College, which leads to the Certified Marketing Research Professional (CMRP) designation.

Articulation Agreements
Start with a graduate certificate, and continue to a master of business administration through our degree completion partnership. Successful graduates of this Marketing – Research and Analytics program may choose to continue with courses leading to a graduate degree.

Career Outlook
-Marketing research specialist or analyst
-Research analyst
-Marketing research and intelligence coordinator
-Market intelligence specialist or analyst
-Customer insights analyst
-Consumer research manager
-Business intelligence analyst
-Market research analytics manager
-Web marketing analyst
-Customer experience analyst
-CRM analyst
-Direct response analyst
-Digital marketing analyst
-Social media analyst
-Data and analytics specialist
-Business analytics specialist
-Loyalty program analyst
-Sales analyst
-Marketing strategy analyst

Program Outcomes
-Optimize the financial results produced by interactive marketing programs through the application of marketing analytics
-Contribute to the design of a marketing analytics team project (develop charter, business case financials, technical requirements, design, test plan, test results, approval to proceed) and the management of the resulting project
-Create, manage and mine, and apply modelling and decision making functions to a database
-Utilize data auditing techniques and quality control processes that are consistent with current marketing research codes of conduct and Canadian privacy principles to ensure the integrity of the data collection, storage, analysis and presentation processes
-Compare and contrast, evaluate and select appropriate data sources to meet specific marketing objectives
-Conduct industry, competitor and customer analyses using a wide variety of secondary research sources
-Produce reliable and analyzable data through the application of sound questionnaire design principles to marketing research projects
-Design marketing research projects and interactive marketing programs that are founded in sound sampling techniques, hypothesis testing and research design
-Solve business and marketing problems by identifying, selecting and applying effective, current and relevant techniques such as descriptive and inferential analysis
-Prepare provisional output of analyses including cross-tabulations and pivot tables that address the needs of analysts and prepare final output, including research reports, presentation sides and visual representations of data that address the needs of management
-Develop actionable recommendations based on situation analyses and research findings

Areas of Employment
-Retail corporations
-Organizations with in-house analytical and research functions
-Marketing research firms

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Computer Science influences every aspect of modern life and is one of the fastest-moving academic disciplines. It contributes to everything from the efficiency of financial markets to film and TV graphics and has a huge impact on both economic competitiveness and human wellbeing. Read more
Computer Science influences every aspect of modern life and is one of the fastest-moving academic disciplines. It contributes to everything from the efficiency of financial markets to film and TV graphics and has a huge impact on both economic competitiveness and human wellbeing.


Why study MSc Computer Science at Middlesex?

Our course not only offers a balance between advanced computer science theory and practical experience, but has a very strong focus on contemporary research. Practical work is an important part of every module and the School of Science and Technology has strong links with industry, including companies such as Microsoft and Siemens. The university is very active in the exploration of a number of areas, including computer graphics,mobile development, human-computer interaction, robotics, artificial intelligence, ethics, ubiquitous computing, functional programming, algorithmic biology, image and video analysis, quantum computing, computational biology and visual analytics, and this research influences the course very strongly.

Our course is aimed at students who've studied computing for their first degree, and wish to make themselves stand out further by developing an advanced mastery of the subject.

Course highlights:

The university is home to the Human Interactive Systems Laboratory, acentre of research into haptic technology, and leads the UK Visual Analytics Consortium.

Our specialist multimedia laboratories are well-equipped with industry-standard software and hardware, including both PCs and Macs.

Many of the teaching staff are the authors of widely-used textbooks and learning materials. They include:

Dr Kai Xu, a former senior research scientist with CSIRO, Australia's national science agency;
Dr Elke Duncker-Gassen, aformer systems and software engineer at GEI Gesytec;
Dr Chris Huyck, a former software engineer at Microsoft.
You'll also improve your communication, teamwork, time-management, problem-solving and critical skills.

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Organisations of all sizes must analyse complex business data to remain competitive. Business analytics helps to predict market trends and improve business processes. Read more
Organisations of all sizes must analyse complex business data to remain competitive. Business analytics helps to predict market trends and improve business processes. It empowers managers to make strategic decisions to improve performance in areas such as product development, operations, marketing, sales and supply chain management.

Our MSc Business Analytics develops your analytical skills so you can solve complex business problems. You’re trained to organise, integrate and interpret data so you can make insightful forecasts into all aspects of business operation and implement appropriate actions.

You cover topics such as:
-Statistics and forecasting
-Data mining, visual and analytical techniques
-Global supply networks
-Economic theory
-Business management

Our range of optional modules gives you the opportunity to specialise in a variety of complementary business, management and marketing subjects.

Essex Business School, where this course is taught, is home to the ESRC Business and Local Government Data Research Centre, which helps local authorities and businesses across the UK to harness data more effectively. Not only does the centre have expert data analytics facilities, you’re taught by academics who are actively involved in researching big data and collaborating with businesses to solve real-world issues.

Our School is home to an international community of students and staff. Across our two campuses, our current Masters students join us from more than 40 different nationalities. The University of Essex also offers a number of scholarship and discounts for Masters study, including tailored awards for international applicants.

MSc Business Analytics can be studied on a full-time, part-time or modular basis (ideal if you’d like to gain a qualification whilst in employment).

Postgraduate loans for Masters courses are now available from the Student Loans Company, worth up to £10,000, for students from the UK and EU.

Our expert staff

Our expert academics are at the forefront of the big data debate and reflect this thinking in their teaching.

Essex Business School is in the top 25 in the UK for research excellence (REF 2014) and is recognised for being at the cutting edge of research in: business ethics and corporate social responsibility; organisation studies; leadership and strategy; finance and banking; risk management; and international management.

Specialist facilities

MSc Business Analytics is based at our Southend Campus, with its excellent study and social facilities.

You benefit from being located close to London in the Thames Gateway, one of the UK’s priority areas for economic growth – offering fantastic internship and networking opportunities.

Southend is a seaside town with award-winning beaches, a vibrant night life and excellent transport links. You have access to The Forum, a state-of-the-art building with 24-hour computer suites and study pods. Unlike our Colchester Campus, Essex Business School in Southend is a town centre campus, so you’re amongst the buzz of the High Street, with its excellent bars, shops and restaurants and employment opportunities.

The University of Essex provides initiatives, including those for international students, to help you perform to your best academic potential, such as free academic English classes.

Your future

Data is the driving force behind critical business decisions, so data scientists and analysts are in great demand in both start-ups and well-established companies. Becoming an expert in data analytics means you can help businesses gain competitive advantage by becoming better at making decisions and predictions through organising, analysing, integrating and interpreting data.

This course will equip you with essential numerical, analytical and problem solving skills for a thriving career as a business analyst, manager, or consultant in private and public enterprises.

We have a dedicated employability team within our department, in addition to the University of Essex Employability and Careers Centre, so you are well-placed to find work opportunities both during your course and after you graduate.

In 2015, 78% of our postgraduate taught students were in work or further study (DLHE). Read our graduate profiles to find out the types of organisations our Masters students go on to work for.

Example structure

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

Business Analytics - MSc
-Managerial Economics
-Global Supply Chain and Operations Management
-Business Analytics for Managers and Entrepreneurs
-Applied Statistics and Forecasting
-Research Methods
-Dissertation
-International Business and Strategy (optional)
-The International Business Environment (optional)
-Creating and Managing the New and Entrepreneurial Organisation (optional)

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Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Read more
Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Much of this data contains valuable information, such as emerging opinions in social networks, search trends from search engines, consumer purchase behaviour, and patterns that emerge from these huge data sources.

The sheer volume of this information means that traditional stand-alone applications are no longer suitable to process and analyse this data. Our course equips you with the knowledge to contribute to this rapidly emerging area.

We give you hands-on experience with various types of large-scale data and information handling, and start by providing you with a solid understanding of the underlying technologies, in particular cloud computing and high-performance computing. You explore areas including:
-Mobile and social application programming
-Human-computer interaction
-Computer vision
-Computer networking
-Computer security

You also obtain practical knowledge of processing textual data on a large scale in order to turn this data into meaningful information, and have the chance to work on projects that are derived from actual industry needs proposed by our industrial partners.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

Today’s 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 are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include:
-Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
-Dr Adrian Clark – automatic construction of vision systems using machine learning and evaluation of algorithms, data visualisation and augmented reality
-Professor Maria Fasli – analysis of structured/unstructured data, machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
-Professor John Gan – machine learning for data modelling and analysis, dimensionality reduction and feature selection in high-dimensional data space
-Dr Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
-Professor Massimo Poesio – cognitive science of language, text mining, computational linguistics
-Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students 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

Your future

Demand for skilled graduates in the areas of big data and data science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data.

Our recent graduates have progressed to a variety of senior positions in industry and academia. Some of the companies and organisations where our former graduates are now employed include:
-Electronic Data Systems
-Pfizer Pharmaceuticals
-Bank of Mexico
-Visa International
-Hyperknowledge (Cambridge)
-Hellenic Air Force
-ICSS (Beijing)
-United Microelectronic Corporation (Taiwan)

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

Big Data and Text Analytics - MSc
-MSc Project and Dissertation
-Information Retrieval
-Cloud Technologies and Systems (optional)
-Group Project
-High Performance Computing
-Machine Learning and Data Mining
-Natural Language Engineering
-Professional Practice and Research Methodology
-Text Analytics
-Advanced Web Technologies (optional)
-Data Science and Decision Making (optional)
-Big-Data for Computational Finance (optional)
-Computer Security (optional)
-Computer Vision (optional)
-Creating and Growing a New Business Venture (optional)
-Mobile & Social Application Programming (optional)

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BIM is a growing technology used in the building industry worldwide. It uses digital techniques to create and use intelligent 3D models to communicate building project decisions. Read more

Study a Top UK degree in China

Course outline

BIM is a growing technology used in the building industry worldwide. It uses digital techniques to create and use intelligent 3D models to communicate building project decisions. GIS is a technical system to implement the collection, storage, management, calculation, analysis, display and description of geospatial information data. The results help to understand what is happening in a geographical space which can increase efficiency in building planning and design. The current ‘lack of BIM innovation’ and ‘lack of BIM talent’ could delay the progress of Chinese “smart” cities, which aim to reduce resource consumption and cost and use digital technologies to benefit their citizens.

This programme will be run entirely at the University of Nottingham Ningbo China (UNNC) with internship opportunities in leading BIM companies in China. It is a collaboration between Department of Architecture and Built Environment and Civil Engineering. In particular, research and teaching support will be provided by three leading research laboratories including Geospatial BIM lab, Digital City Infrastructure and technology Innovation Laboratory D-CiTi, and Big Data and Visual Analytics Lab. These laboratories are working closely with leading AEC consultants (Arup, WSP BP), international professional institutions (RICS, ICES, CIBSE) and leading BIM software vendors (Autodesk, Bentley, Leica, Tekla, Trimble).

Students are able to learn how to use and operate a very wide variety of state-of-the-art software, as well as surveying equipment including servo driven total stations, laser scanners, GNSS, digital and analogue photogrammetry. With extensive project and consultancy experience on the Geospatial Engineering, BIM in the AEC sector in the UK and China, the team is planning to promote the Smart City with multi-dimension BIM applications across China.

Advantages of studying this programme at the University of Nottingham Ningbo:

1. be familiar with BIM related software and surveying device
2. the ability to apply their skills directly within the surveying and AEC industry
3. react quickly to new technologies and innovations
4. communicate ideas effectively in written reports, verbally and presentations to groups
5. exercise original thought, as well as gain interpersonal, communication and professional skills
6. participate real project work for experience accumulation
7. plan and undertake individual projects


This programme will help:

1. gain a complete understanding of theory, practice and issues of BIM and Geospatial technologies
2. acquire opportunities to use what you have learnt in real project work
3. explore new research methodology to promote development in this field
4. acquire technical skills of software operation, data analysis and design optimization
5. improve team-work ability and communication skill
6. foster individual ability to conduct academic researches

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Make your input really count as a postgraduate student in the Department of Computer Science and Engineering. Traditional computer science research covers the hardware and software of computer systems and their applications. Read more
Make your input really count as a postgraduate student in the Department of Computer Science and Engineering. Traditional computer science research covers the hardware and software of computer systems and their applications. Computer science programs at HKUST emphasize an integrated approach to the study of computers and computing methods to collect, process, analyze and transmit information to support relevant and useful applications in modern life.

The Department's goal is to offer a full range of postgraduate courses and research projects to meet the needs and interests of our students and to help solve relevant problems for society. Our world-class faculty members engage in cutting-edge research at the heart of the information technology revolution and our postgraduate students are involved in both applied and fundamental research. The Department has 50 full-time faculty members and 180 postgraduate students.

Computer science is still a young field. The world is only just beginning to realize the potential of information technology. The Department and its programs prepare students to meet the exciting challenges that await and to generate new advances in computing that will fuel future progress.

The MPhil program seeks to strengthen students' knowledge in computer science and expose them to issues involved in the development, scientific, educational and commercial applications of computer systems. Students are required to undertake coursework and successfully complete a thesis to demonstrate competence in research.

Research Foci

The Department's research involves many different areas:
Artificial Intelligence
Machine learning, data mining and pattern recognition, knowledge representation and reasoning, robotics and sensor-based activity recognition, multi-agent and game theory, and speech and language processing.

Data, Knowledge and Information Management
Large-scale data management, modeling and distribution encompassing web query processing, information retrieval and web search, data mining, enterprise systems, high-performance data management systems on modern computers, and database support for science applications.

Human-Computer Interaction
Augmented reality, multi-touch interaction, crowdsourcing, multimodal communication, affective computer, visual analytics of big data, intelligent interface for robots, E-learning, healthcare and e-commerce.

Networking and Computer Systems
Pervasive computing and sensor networks, peer-to-peer computing, grid computing, high-performance switches and routers, video delivery and multicasting, multimedia networking, MAC protocols for ad-hoc networks, web cache management, DDOS detection and defense, and resource management and allocation in optical networks.

Software Technology and Applications
Software engineering, data mining for software analysis and debugging, computer music, cryptography and security, internet computing.

Theoretical Computer Science
Combinatorial optimization, performance analysis techniques, computational geometry, formal languages and machines, graph algorithms, and algorithmic combinatorial game theory.

Vision and Graphics
Computer vision, computer graphics, medical image analysis, biometric systems, and video processing.

Facilities

The Department has excellent facilities to support its programs and is committed to keeping its computing facilities up to date. There are about 700 workstations and PCs, including those in four teaching laboratories, three MS Windows Labs and one Linux lab. The Department also runs several research laboratories with specific facilities, including the computer engineering, database, Human-Computer Interaction Initiative, vision and graphics labs. Specialized project laboratories include:
-The HCI lab, has a 360 degree circular CCD-camera capturing system with a 4x3 large display array and 120" rear projected 3D active stereo system, and large-sized multi-touch panels, linked with various physiological sensors for gesture/body tracking;
-The Human Language Technology Center, with various corpora and a Linux cluster;
-The System and Media Laboratory, partially funded by Deutsche Telekom, focusing mobile computing and any interesting topics related to social network; and
-The Networking group, that maintains different sets of network cluster for Data center and cloud computing research.
-Different research groups maintain their own CPU/GPU cluster customized for different research need.

In addition, the Department manages a pool of Linux servers as CPU/GPU cluster for general research projects demanding significant system resources, and acquires a GPU cluster for the whole University. The file servers are connected with one HDS AMS2100 and one HDS HUS110 Storage Area Network (SAN), with a total capacity of more than 60TB. There is also a pool of high performance servers with GPUs dedicated for undergraduate courses on parallel computing and Big Data analysis.

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The HKUST MSBA program aims to meet the increasing demand for business analytics professionals. It provides students with strong knowledge of business analytics by bringing together a wide range of knowledge in applied statistics, information management, optimization, and modelling. Read more
The HKUST MSBA program aims to meet the increasing demand for business analytics professionals. It provides students with strong knowledge of business analytics by bringing together a wide range of knowledge in applied statistics, information management, optimization, and modelling.

The program focuses on teaching students how to make good use of information and business analytics knowledge for building data-driven strategies, enhancing performance and facilitating evidence-based discussion for problem solving. It develops students’ business analytics competency and hands-on experience in solving real business problems in various areas such as finance, marketing, healthcare, etc.

Our expectation is that graduates from the program are able to integrate cross-disciplinary knowledge with analytics to manage complex data structures in a business environment; analyze real business problems using various business analytics tools and play leading roles in successful business strategy execution using analytics.

Curriculum

Students will acquire necessary analytics skills in the required courses and focus on their areas of interest in elective courses. Both full-time and part-time students are required to complete a total of 30 credits from required and elective courses to graduate.

Required Courses
-Big Data Analytics
-Business Analytics in R
-Business Modeling and Optimization
-Consumer Privacy Management in the Information Economy
-Data Analysis
-Introduction to Business Analytics
-Simulation for Risk and Operations Analysis
-Social Media and Network Analysis
-Visual Analytics for Business Decisions

Elective Courses
-Big Data Technologies
-Business Analytics Practicum
-Business Modeling with VBA
-Digital Marketing Strategy and Analytics
-Electronic Commerce and Web Analytics
-High Dimensional Statistics with Business Applications
-Operations Analytics
-Project Management
-Special Topics in Business Analytics

About Hkust Business School

Established in 1991, the School of Business and Management at the Hong Kong University of Science and Technology (HKUST Business School) is young, dynamic and very well respected for the quality of its programs and the impact of its research.

We are the first business school in the region to be awarded accreditation by both the US-based Association to Advance Collegiate Schools of Business (AACSB International) and the European Quality Improvement System (EQUIS). The degrees offered by the HKUST Business School are recognized worldwide.

We are recognized as “Asia’s youngest but most respected business school” (Financial Times). Our programs are highly regarded for their cutting edge design and delivery, and are consistently ranked among the very best in the world by international media.

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Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business. Read more
Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business.

Whether it’s using Artificial Intelligence to improve a chess programme, or understanding the power of visualisation from a simple graph.

With input from industry experts in class and on-site, you will engage with real-world business problems.

PROGRAMME OVERVIEW

Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions.

This programme will take your career to the next level and develop your ability to confidently make high impact businesses decisions that are driven by data.

The programme focuses on two key areas: analysing business data, and solving business challenges analytically. Optional modules allow you to further specialise in areas such as the economic, managerial or finance or aspects of the subject.

Furthermore, you will benefit from hands-on experience of a wide range of analytics software such as simulators and mathematical tools.

PROGRAMME STRUCTURE

The programme is studied full-time over one academic year. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analytics
-Supply Chain Analytics
-Econometrics I
-Machine Learning and Visualisations
-Principles of Accounting
-Foundations of Finance: Finance and Investments
-Supply Chain and Logistics Management
-Information for Decision Making
-Managing Decisions Implementation
-Introduction to Marketing Analytics
-Econometrics II
-Business Process Management
-Innovation Management
-Investment Analysis
-Dissertation

CAREER PROSPECTS

Business analytics students often pursue careers as consultants, researchers, managers, and analysts.

SOFTWARE

You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:
-Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
-COGNOS and SQL Server for Business Intelligence for analytical processing
-Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
-SAP for Enterprise Resource Planning
-R, SPSS and EViews for coding, statistics and forecasting
-ILOG’s Optimisation Studio (Cplex) for optimisations
-Matlab for algorithms and programming and Simulink (SimEvents) for simulations
-Arena (or Simul8) for Discrete Event Simulations

EDUCATIONAL AIMS OF THE PROGRAMME

The programme’s aim is to provide a high quality education that is both intellectually rigorous and at the forefront of management science research, relevant for problem solving and decision making by managers.

It will respond to the emergent needs of corporations and academia for professionals who are able to work with analytical tools to generate value from available Information depots and take advantage of the vast amounts of data now provided by the modern ICT and ERP systems, which underlie the operations of modern corporations.

The program will implant understanding of the theoretical base around knowledge management and knowledge work, practical skills and experience in using analytical software tools.

It will allow future professional managers and consultants to cope with an increasingly complex and global operational environment of the modern corporation.

Completion of the programme will provide a sound foundation for those considering continuing their academic development towards a PhD degree in the management disciplines.

The programme is structured in a way that would provide students with a choice between a more quantitative intensive track of modules or a qualitative analytic (business development track) which would reflect students’ personal strengths and preferences and match future career aspirations.

The compulsory modules provide a sound foundation which builds an analytical skillset using relevant statistical and management theories, and supports the development of practical hands-on experience applying the theoretical aspects using real-world data to address corporate challenges and find solutions to actual problems.

The readings in the module will build a sound basis which would allow students to access and understand the academic literature and undertake empirical investigations in the areas of decision modelling and business development.

PROGRAMME LEARNING OUTCOMES

The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:

Knowledge and understanding
-A systematic, in-depth understanding of the development; issues and influences relevant to discipline of Management Decision Making, Management Science, and Data Science.
-Deep and thorough understanding of quantitative analytical methodologies and hands-on experience with decision-making software and data management tools.
-Knowledge about issues, application and analysis of Big Data
-An understanding of the academic research process.

Intellectual / cognitive skills
-Demonstrate deep learning, understanding of the material and ability to apply the knowledge and demonstrate skills in problem solving in the topic space of the modules studied
-Carry out assessments of data in a repository, select the appropriate analysis tools, design and execute an analytical methodology (not required for PG Certificate), apply adequate visualization methodologies to present the results and interpret the findings and finally to communicate the results effectively to a select audience

Professional practical skills
-Demonstrate the ability to independently evaluate critical approaches and techniques relevant to Business Analytics
-Know and apply a range of techniques and tools to analyse data related to business operations
-Capability of selecting the right methodology and software to solve management and operational business issues
-Relate existing knowledge structures and methodologies to analytical business challenges

Key / transferable skills
-Conduct critical literature review; to select, define and focus upon an issue at an appropriate level
-Develop and apply relevant and sound methodology
-Apply the methodology to analyse the issue
-Develop logical conclusions and recommendations
-Be aware of the limitations of the research
-Identify modifications to existing knowledge structures and theoretical frameworks and therefore to prose new areas for investigation, new problems, new or alternative applications or methodological applications

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

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Data science is positioned to be the major new arena of scientific discovery in the 21st century. As Eric Schimdt, CEO of Google, has pointed out, we now generate as much information every two days as we did from the dawn of human civilisation up until the year 2003. Read more
Data science is positioned to be the major new arena of scientific discovery in the 21st century. As Eric Schimdt, CEO of Google, has pointed out, we now generate as much information every two days as we did from the dawn of human civilisation up until the year 2003.

To cope with this vast amount of data, there is an urgent requirement to derive meaningful insights from very large and diverse data sources: the so-called 'big data challenge'. Secure identification of individuals, accurate financial prediction and reliable cancer diagnosis are all examples of area in which the technologies underpinning data science are marketing revolutionary contributions, enriching our lives, and making our future healthier, more efficient and more secure.

The MSc by Research (Data Science) provides an opportunity for students who wish undertake an individual real-world big-data research project; you can choose a project area from a wide variety of interdisciplinary domains, including business information systems, e-health, social media, cloud computing, smart homes, intelligent vehicles and ambient assisted living.

We currently have supervisors available in:

Machine learning
Visual analytics
Mathematics and statistics
Smart sensors (for ambient assisted living and smart homes)
Numerical algorithms (especially computational geometry)
Artificial intelligence
Ethics of technology
Psychology
Business information systems and business informatics
Robust software/system development
Networking
Ethics
Marketing
Operations management
Enterprise and economic development
Education
Design
As a student of this course you'll receive a free electronic textbook for every module.

Read less
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). Read more
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organization with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of core methods in data science application and research (eg bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (eg NoSQL data stores) alongside cloud computing tools (eg Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (eg to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation (including Research Methods)

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

As preparation for the dissertation, you will be given a grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research.

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This professional degree is for people who are passionate about drawing meaningful knowledge from data to drive business decision-making or research output. Read more
This professional degree is for people who are passionate about drawing meaningful knowledge from data to drive business decision-making or research output.
Data is a vital asset to an organisation. It can provide valuable insights into areas such as customer behaviour, market intelligence and operational performance. Data scientists build intelligent systems to manage, interpret, understand and derive key knowledge from this data.
For those with strong mathematical or quantitative backgrounds, this degree will develop your analytical and technical skills to use data science to guide strategic decisions in your area of expertise. It offers the flexibility to tailor your learning to your professional and personal interests.
Leveraging the University’s research strengths, you will explore the latest in data mining, machine learning and data visualisation, while developing the skills to effectively communicate data insights to key stakeholders.
For those with qualifications in other areas such as health and education, a Graduate Certificate in Data Science can provide you with data science capability to complement your existing skills and provide a pathway to the master’s program.
Course structureThe course comprises core units, elective units and a capstone project where you will apply your skills to a real-world data science problem. You can tailor your degree by selecting elective units and a project that complement your particular interests, background and qualifications.
Core units for the Master of Data Science include Principles of Data Science, Computational Statistical Models, Visual Analytics, and Knowledge Discovery and Data Mining.
For the Graduate Certificate in Data Science, core units include Principles of Data Science, Algorithms, Database Management Systems and Introduction to Statistics.
You can select elective units from the following data science subjects, or from other disciplines relevant to your background and qualifications.
Data science electives include:
Advanced Data Models
Cloud Computing
Multimedia Retrieval
Data Analytics and Business Intelligence
Information Security Management
Statistical Learning and Data Mining
Statistical Natural Language Processing
Predictive Analytics.We also offer a pathway for eligible candidates planning to pursue a research degree.

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