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Masters Degrees (Data Analyst)

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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. Read more

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:

  • A firm grounding in the theory of data mining, statistics and machine learning
  • Hands-on practical real world applications such as social media, biomedical data and financial data with Hadoop (used by Yahoo!, Facebook, Google, Twitter, LinkedIn, IBM, Amazon, and many others), R and other specialised software
  • The opportunity to work with real-world software such as Apache

Modules & structure

You will study the following core modules:

You will also choose from an anually approved list of modules which may include:

Skills & careers

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: 

  • Data Scientist
  • Data Mining Analyst
  • Big Data Analyst
  • Hadoop Developer
  • NoSQL Database Developer
  • R Programmer
  • Python Programmer
  • Researcher in Data Science and Data Mining

Find out more about employability at Goldsmiths.



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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). Read more

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.

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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Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Read more
Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Study on a course which has been developed with direct input from industry experts who will bring real life business case scenarios to you.

More about this course

This specialist advanced course will equip students with the theoretical, technical and practical data analytics competencies required in an area of economic growth. The course curriculum content has been developed with direct input from industry experts and utilises specialist software tools and techniques. Students’ experience of the course will be enriched with exposure to real life business case scenarios brought to them by skilled professionals in industry.

The specialist nature of the course will allow students to explore and experience advanced techniques in data science. Students will acquire practical skills, often first-hand from an external practitioners, preparing them for employment as data analysts. Students will also be trained in the use of software tools and environments currently used by the industry sector. For example, students on this course will have exposure to R and Python programming, IBM SPSS, SAS®, Tableau, Oracle and Hadoop.

A range of assessment methods are used on the course, including written reports, practical and research assignments, demonstrations, presentations, group work and examinations.

Modular structure

The modules listed below are for the academic year 2016/17 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

Year 1 modules include:
-Data Analysis and Visualization (core, 20 credits)
-Data Mining for Business Intelligence (core, 20 credits)
-Data Modelling and OLAP Techniques for Data Analytics (core, 20 credits)
-MSc Project (core, 60 credits)
-Programming for Data Analytics (core, 20 credits)
-Statistical Modelling and Forecasting (core, 20 credits)
-Financial Mathematics (option, 20 credits)
-Work Related Learning (option, 20 credits)

After the course

On completion of the course graduates will be well equipped to work in some of the fastest growing sectors of the data science and big data industries. The course offers wide-ranging career opportunities in the commercial industry, public and financial services, especially in areas requiring big data analysis such as consumer, healthcare, scientific, financial, security intelligence, business and social sciences.

Job roles include data scientist, data analyst, digital analyst, big data consultant, statistical analyst and data modeller. Graduates will be eligible to work as data analysts or data scientists in a multitude of areas where skills such as R or Python programming, machine learning and statistical modelling, SAS® and SPSS experience, data visualisation and data-driven decision-making are required.

The course also provides an excellent basis for further study for those wishing to pursue a higher-level research degree or embark on an industry-based research career.

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Data and information is recognised as a central to the economic, business and cultural life of our society today. This course will equip you with the advanced information and analytic skills to thrive in the digital knowledge economy. Read more

Data and information is recognised as a central to the economic, business and cultural life of our society today. This course will equip you with the advanced information and analytic skills to thrive in the digital knowledge economy.

Whether you are already working and looking to enhance your expertise in the area of data science or you have recently graduated and want to move into roles specifically aligned with data science and analytics, this course will help you develop a comprehensive understanding of the role of data within business. You will study data governance, considering how data can be sourced, consolidated and stored securely, responsibly and efficiently. You will also explore the techniques used to analyse data, discover how data can contribute to a company's business strategy and focus on the application of data science in a specific field such as health, education or fraud prevention.

Central to the course will be the themes of security, ethics, data governance and sustainability. You will graduate with a critical awareness of the current ethical and security problems associated with the exploitation of information services and resources in organisations, enabling you to support collection, sorting and ordering of data, big data and information across a range of sectors.

Course Benefits

In the ever changing technological world, the skills required to successfully manage and share an organisations' data, require employees that have good understanding of data issues, technology, people and business. All of these skills are addressed by modules on this course, placing you at the forefront of the emerging digital economy.

You will be encouraged to undertake projects and volunteering opportunities with outside organisations, and our expert staff will ensure your learning is highly relevant to the workplace. Industry experts and leaders in their field will further fine tune your knowledge by sharing their expertise and professional insights during regular guest lectures. Past speakers have included computing, forensic and engineering experts from KPMG, Hermes Innovation Lab and Premier Farnell. Goranka Bjedov, Performance and Capacity Engineer at Facebook, visited the School to talk about her work in ensuring Facebook's worldwide data centres are able to deliver a 24/7 service to users.

The University is home to four research institutes and 14 research centres, including the Cybercrime & Security Innovation Centre and the New Technology Institute. Findings from our research will feed into your learning to equip you with the latest thinking and industry insights.

Indicative core modules

  • Dissertation / Masters Project
  • Research Practice
  • Data Analysis & Visualisation
  • Project Management
  • Critical Perspectives on Information
  • Database Systems

Indicative option modules

  • Cloud Computing
  • Data Warehouse Models & Approaches
  • Business Intelligence
  • Green Computing Strategies
  • Digital Security
  • Negotiated Skills Development
  • Managing Information in the Digital & Global Environment

Job prospects

With your combination of business awareness, management skills, technology knowledge and understanding of data science, you will be well prepared to pursue a career as a data manager across a range of sectors. The option modules available on the course will allow you to follow a route that suits your skills, aspirations and interests. Recent students from the School have secured roles with the NHS and local councils in data management and business intelligence, while others have gone on to start their own consultancy businesses. Further study for a PhD in the area of data science is also an option.

  • Database engineer
  • Data quality controller
  • Data scientist
  • Retail / fraud / health / business data analyst


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Develop the skills you need for a career in this fast emerging field of data science. This programme will deepen your understanding of advanced software development, systems for big data analytics, statistical data analysis, data mining, data privacy and security, data visualisation and exploration. Read more

Develop the skills you need for a career in this fast emerging field of data science.

This programme will deepen your understanding of advanced software development, systems for big data analytics, statistical data analysis, data mining, data privacy and security, data visualisation and exploration.

It is an interdisciplinary programme, designed for students with a first degree in subjects such as:

  • Computer Science
  • Electrical Engineering
  • Mathematics
  • Physics
  • Economics

Our teaching is strongly linked to research within the Faculty of Engineering and Informatics, which includes aspects of applied computing, theoretical computer science, and communications and networks. Statistical analysis of data across various disciplines is a central theme of our research.

You can tailor your studies to particular areas of interest or career aspirations through our range of optional modules, and in the final dissertation (which is a significant piece of project work).

On graduation you’ll be ready and able to develop solutions to challenges in big data analytics and big data systems.

What you will study

The programme is intended to equip graduates with the cutting-edge knowledge and skills to work in the industry as a Data Scientist, Big Data Architect, or Big Data Analyst.

Core Modules

Option Modules

Learning and assessment

You'll learn through a mixture of formal lectures, practical lab sessions, tutorials and seminars.

Some modules involve supervised group work, usually with an assigned academic staff member for each group.

Most modules are related to research in the school.

All modules require students to undertake independent study, supported through distance learning technologies such as our Virtual Learning Environment. Reading lists and suggested resources for independent study provide further direction for students to undertake this work, and regular contact hours and informal feedback throughout the courses provide opportunities for further guidance for learners.

Assessments for modules mostly take the form of practical coursework, lab tests and written exams, with all forms being well represented across all modules.

Facilities

Our facilities are impressive, with several laboratories filled with dual-screen, dual boot (Windows and Linux) systems packed with industry-standard software.

Our specialised labs, such as the Ethical Hacking lab and the Internet of Things lab, allow students to build their skills within these key areas of growth, in a structured way through taught modules.

We provide a range of online facilities to support independent learning, including our Virtual Learning Environment which gives you access to learning materials and collaborative learning tools 24/7, anywhere in the world. We also provide virtual server technology using in house hardware to allow students to use our operating systems remotely.

Career prospects

Big data is a major area for future growth and investment. As the global big data industry continues to grow year after year we continue to grow our programme, and continue to produce future leaders in an exciting and rewarding field.

The University is committed to helping students develop and enhance employability and this is an integral part of many programmes. Specialist support is available throughout the course from Career and Employability Services including help to find part-time work while studying, placements, vacation work and graduate vacancies. Students are encouraged to access this support at an early stage and to use the extensive resources on the Careers website.

Discussing options with specialist advisers helps to clarify plans through exploring options and refining skills of job-hunting. In most of our programmes there is direct input by Career Development Advisers into the curriculum or through specially arranged workshops.

Study support

We have a commitment to strong pastoral care for all of our students, which includes a Personal Tutor for all students, regular contact hours for tutor groups and our supportive student service teams who are always ready to help with any questions and provide the advice that you need.

In addition to standard study support through taught sessions, our Virtual Learning Environment allows students to access resources, participate in group work and submit work from anywhere in the world 24/7.

University central services are rich with support teams to assist students with every aspect of their journey through our degree programmes. From our Career and Employability Service, through our strong Students' Union, to our professional and efficient Student Finance team, there are always friendly faces ready to support you and provide you with the answers that you need.

Research

There is much research taking place at the Faculty of Engineering and Informatics related to this Master's programme. This includes aspects of applied computing, theoretical computer science, and communications and networks. Statistical analysis of data across various disciplines is a central theme of our research.

Teaching informed by research is at the core of this programme. Graduates leave us well prepared to pursue academic research, or industry based research and development positions.



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Role of a data scientist. We live in a data-driven world that’s generating huge volumes of information at ever-increasing rates, via social media, financial transactions, telecommunications, and even scientific discovery. Read more

Role of a data scientist

We live in a data-driven world that’s generating huge volumes of information at ever-increasing rates, via social media, financial transactions, telecommunications, and even scientific discovery.

The emerging interdisciplinary field of data science combines areas of computer science with mathematical statistics and domain expertise to manage and analyze this data.

Though incorporating statistical methods, data science puts a greater emphasis on the specialized computational skills required to manage and analyze big data from sources such as social media, sensors, mobile and transaction data.

Data scientists develop the capability to derive insight and opportunity from the vast repositories of data that many organizations collect. They help organizations in all sectors of the economy to make sense of these very large volumes of data; enabling businesses to gain a competitive edge, governments to deliver more targeted services, and research teams make new discoveries.

Career outlook

As data science is still a new and emerging field, the roles available for data scientists are quite varied and diverse. As well as the title of data scientist, other positions include: analytics specialist, business intelligence analyst/developer, data analyst, data architect, data engineer, data miner, research scientist and web analyst.

Program structure

The first year of the program develops a solid foundation in computer science and statistics –

core skills necessary for every data scientist in their professional work.

Students with an undergraduate background in computer engineering may gain some advanced standing. During the first year of this master's degree, students with an undergraduate background in Mathematics will take advanced courses in computer science to supplement their knowledge. While students with a bachelor's degree in computer science will follow specialized courses of applied mathematics in order to get ready for the advanced core courses of the second year.  

The second year also includes a major project, which can be working on an industry or research project.

International Students

Find out more about studying as an international student at USJ here: https://www.usj.edu.lb/sri/index_en.php



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The internet and advances in digitalisation and social networking are transforming how companies interact with customers and partners. . Read more

The internet and advances in digitalisation and social networking are transforming how companies interact with customers and partners. The specialization in Digital Marketing and Data Science provides participants with the strategic and analytical skills to successfully guide companies’ strategies in a digital world that is overflowing with data on customers, products and interactions.

The MSc in Digital Marketing & Data Science is designed to grow a new generation of leading marketing specialists – digital savvy professionals that can benefit from an explosive growth of online technologies to develop business.

The program pedagogy uniquely combines a strong academic background in business studies, marketing, data analysis and strategy with an in‐depth and specific digital knowledge in online video, mobile, viral, social media, and data driven marketing. As a student, you will also have the possibility to learn the latest innovations from major players like Google, Facebook, Amazon, Twitter, Netflix, and other greatest technological companies or discover the benefits and challenges faced by the main companies.

This program focuses on digital marketing and data analytics, which includes business analytics (with advanced Excel and Tableau Software), coding (R and Python), database access (SQL), data science and machine learning with Python. You will be able to manage the coming technological and algorithmic disruptions in marketing, instead of being made redundant by them.

During the first semester the focus is on the fundamentals of digital marketing and about becoming proficient in data analytics. The second semester covers strategy, value intensive processes in digital marketing and data analytics and the third semester opens specializations and training in a professional and international context.

Careers

Upon completing the MSc in Digital Marketing & Data Science, you will become accomplished digital marketing professionals, able to manage and innovate in a data‐rich business environment. You will be well‐prepared to work in a sales or marketing department of startups and major brands in business to consumer or business to business environments. You will also fit the requirements of advertising agencies looking for marketing professionals knowledgeable about multichannel communication, consulting firms managing the digital transformation of their clients, as well as digital media and technology companies looking for managers with a strong business background who are also familiar with their trade.

The skills developed with the program open large horizons as all companies are now challenged by data and digital disruptions.

The MSc in Digital Marketing & Data Science will prepare you for specific positions and job titles related to the program’s fields, with target positions in marketing and business updated to the digital era such as :

  • Digital marketing manager
  • Marketing product manager
  • Head of digital solutions
  • Digital project manager
  • Data analyst
  • Business intelligence analyst
  • Digital analyst
  • Consultant in digital strategy
  • Consultant or project manager in digital transformation
  • Social media manager
  • Online media planner
  • Web project leader

Admissions

Selecting the right student is about more than just test scores. At emlyon business school we take an applicant's entire potential into account. Elements like motivation to pursue the MSc in Digital Marketing & Data Science, your background and career aspirations weigh just as heavily in our selection procedure.

If you would like to have the contact details of the right person to help you with any questions regarding the program or the selection procedure, you can create your personal space and obtain full contact details on your emlyon business school dashboard.

Admission Process

The first step of the admission process is your online application, which you can access through your personal space(programme dashboard).

  • Selection sessions for the 2018 intake run from November 2017 to July 2018.

Should your application be complete, you will receive the admission board’s final decision within 15 working days. Please send back your enrolment form within 10 working days.



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This course produces specialist Data Scientists who can design and implement computer-analytics and visualisation solutions for industry. Read more

This course produces specialist Data Scientists who can design and implement computer-analytics and visualisation solutions for industry. With an emphasis on Big Data, you will gain an understanding of the needs of businesses and how to manage requirements in this emerging international area of focus for companies, governments and economies.

The course focuses on Big Data, traditional and unconventional data management, including acquisition, storage, warehousing, analytics and visualisation tools and techniques. You’ll put these skills into practice in our state-of-the-art facilities.

Importantly, the course satisfies industry’s demand for Data Scientists who have the ability to relate key performance indicators, and contribute to business decision-making at a high level – giving you an advantage in the job market.

What you will study

Throughout the course, content is complemented by practical work, allowing you to support your theoretical knowledge with practical experience in data storage, mining, warehousing, visualisation and analysis as well as transferrable skills.

The individual project provides an opportunity for applying specialist knowledge together with analytic, problem-solving, managerial and communication skills to a particular area of interest within data science. Working with the full support and guidance of an allocated Project Supervisor, you will propose, plan, specify, develop, evaluate, and present a substantial project.

Teaching and assessment

You will be taught through a mixture of lectures, tutorials, labs. You will be invited to attend talks presented by highly-experienced researchers, speakers from industry, and members of the BCS (British Computer Society) on a wide range of industry-related topics. You will also be supported through our online virtual learning environment where you can access a wide variety of resources and other support materials.

Part-time September start students

  • A) Part-time September start students who would like to work on their project during the summer start their project in May of their second year and complete in December of their third year. (Approximately 28 months)
  • B) Part-time September start students who would like to avoid working on their project during the summer start their project in September of their third year and complete in May that year.  (Approximately 33 months)

Part-time January start students

  • C) Part-time January start students who would like to work on their project during the summer will start their project in January of their third year complete their project in August that year. (Approximately 32 months)
  • D) Part-time January start students who would like to avoid working on their project during the summer will start their project in January of their third year, have a break during the summer semester and continue their project September-December that year. (Approximately 36 months)

ACTIVITY SUMMARY

  • Lectures - 30 students per group, 24 hours over two months
  • Tutorial - 30 students per group, 24 hours over two months

INDEPENDENT STUDY

A significant portion of this courses is underpinned by independent learning, and you will be expected to be responsible for managing your coursework, reading and other learning activities appropriately.

STAFF DELIVERING ON THIS COURSE

Our staff members feature in the annual Support and Teaching staff with Appreciation and Recognition (STAR) awards voted by the students and organised by RGU:Union. Recently we have been awarded two Lecturer of the Year awards and an award for Continued Excellence.

Many of our academic staff are Fellows or Senior Fellows of the Higher Education Academy or are working towards this accolade. This is a professional recognition of practice, impact and leadership of teaching and learning.

Staff on this course could also include: visiting lecturers, visiting Distinguished Researchers, library staff and industry experts and postgraduate researchers.

ASSESSMENT

Typically students are assessed each year:

Year 1

  • 4 written exams, typically for 3-4 hours
  • 2 reports
  • 1 dissertation (final year project)
  • 1 oral assessment
  • 8 practical skills assessment
  • 1 group critique

Placements

Students who perform particularly well during their first semester of studies will be invited to apply for any long-term placement opportunities (40-45 weeks) found by the Placement Office. Alternatively, you can seek your own long placement or a short placement. Note that permission to undertake a placement is at the discretion of the School and students who optionally go on placement also need to pay a £1000 fee.

Job prospects 

The opportunity to exploit Big Data is recognised world-wide, and it is included in government economic strategies. The UK Government and Scottish Governments highlight it as an emerging opportunity for growth. The course prepares you for a career in Data Science or in Big Data. Job openings include: Data Scientist, Data Analyst, Data Visualisation Specialist, Data Manager, Database Designer/Manager, Data Mining Expert and Big Data Scientist. The course also prepares students for research careers by providing the skills necessary of an effective researcher. Suitable MSc graduates may continue to PhD programmes within the school.

Aberdeen is home to many multinational oil and gas companies and associated suppliers such as mainstream software houses, IT providers to major oil-related companies, specialist software consultancies, and venture capital start-ups, so graduates can seek employment locally. RGU is also involved in a number of commercial collaborations on a local, national and international scale with organisations such as BP, British Geological Survey, Wood Group PSN, Accenture, WIPRO and many Aberdeen-based software development companies, and you may benefit from the university’s links with some of these companies when it comes to securing placements or employment.

Please visit the website to find out how to apply.



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This Masters degree provides you with a strong conceptual and theoretical understanding of big data analytics. You will gain the essential skills and confidence required to apply and produce knowledge and understanding of issues surrounding big data analytics in a range of contexts. Read more
This Masters degree provides you with a strong conceptual and theoretical understanding of big data analytics. You will gain the essential skills and confidence required to apply and produce knowledge and understanding of issues surrounding big data analytics in a range of contexts. This will enable you to evaluate, adapt, create and utilise appropriate models, methods, practices, theories and computational techniques in the face of changing and evolving technology. There is the opportunity to develop a critical understanding of visualisation concepts, modelling and algorithmic foundations, as well as to develop and evaluate new or advanced bespoke solutions for processing, analysing and making sense of big and/or complex data. The programme enables you concentrate on a specific practical area within computer science and is suitable whether you are a recent graduate or already working in the IT industry and looking to change career paths.

What will I study?

Gaining an in-depth and systematic knowledge of big data management theories, concepts, methodologies and professional practice, you will develop a systematic and critical understanding of algorithms and programming techniques for processing, storing, analysing, visualising and interpreting data.

You will learn the practical skills of mathematics that underpin the processing of data, the programming applications required to manage big data, and the visualisation techniques necessary to make sense of large data sets. There will also be the opportunity to work with emerging technologies derived from industry.

How will I study?

The course is delivered through a combination of lectures, seminars and tutorials with a mixture of daytime and evening classes. Sessions will frequently be highly interactive with a focus on the practical application of concepts and the use of case studies drawn from real life. An emphasis on small group sizes ensures that you will have plenty of opportunities for individual discussions with your tutors. Typically, you will study for approximately nine hours a week if you are studying on a full-time basis.

How will I be assessed?

Your vocational capability, academic critical thinking and intellectual development will be assessed throughout the course. This is achieved through a combination of coursework, case studies, problem-solving exercises and examinations. You may be assessed individually or as part of a group.

Who will be teaching me?

You will be taught by highly qualified, experienced and enthusiastic academic staff who are research-active and fully engaged with the wider business and academic community. The programme team specialise in a variety of subjects so you will benefit from a wide range of expertise. There will also be occasional input from external IT professionals who will be invited to teach particular sessions.

What are my career prospects?

As organisations become ever more dependent on data, there are increasing opportunities in specialist positions related to obtaining, processing and visualising data.

The MSc Big Data Analytics provides you with the skills and knowledge to develop your interests for a career in data science. You will be ideally placed to progress into roles where you will work as a data scientist or data analyst.

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The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain. Read more

The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain.

About this degree

Students develop an understanding of the principles underlying the development and application of new techniques in this area, alongside an awareness of, and ability to analyse the range and scope of algorithms and approaches available, and design, develop and evaluate appropriate algorithms and methods for new problems and applications.

Students undertake modules to the value of 180 credits.

The programme consists of one core module (15 credits), five to seven optional modules (75 to 105 credits), up to two modules (30 credits) from electives, and a research project (60 credits).

Core modules

  • Supervised Learning (15 credits)

Optional modules

Students must choose 15 credits from Option Group One and a minimum of 60 credits from Option Group Two. Students must choose a further 30 credits from either Option Group Two or approved electives.

Option Group One (choose 15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Option Group Two (choose 60 to 90 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Affective Computing and Human-Robot Interaction (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Bioinformatics (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Programming and Mathematical Methods for Machine Learning (15 credits)
  • Statistical Natural Language Programming (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

Students may select up to 30 credits from elective modules

A list of acceptable elective modules is available on the departmental website.

Dissertation/report

All MSc students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words in the form of a project report.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed though a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Further information on modules and degree structure is available on the department website: Machine Learning MSc

Careers

Graduates from this programme have an excellent employment record. Substantial sectors of UK industry, including leading, large companies already make extensive use of intelligent systems techniques in the course of their business activities, and the UK has a number of very successful developers and suppliers of the technology. Students also benefit from strong corporate and academic connections within the UCL Computer Science alumni network.

Graduates have taken machine learning research degrees in domains as diverse as robotics, music, psychology, and bioinformatics at the Universities of Basel, Cambridge, Edinburgh, Nairobi, Oxford and at UCL. Graduates have also found positions with multinational companies such as BAE Systems and BAE Detica.

Recent career destinations for this degree

  • Computer Vision Engineer, ZVR
  • Data Analyst / Data Scientist, Deloitte Data Analytics Group
  • Programmatic Yield Manager and Data Analyst, eBay
  • Data Scientist, dunnhumby
  • PhD in Computer Science, UCL

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. Machine Learning graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our graduates. Similarly graduates now work in companies in Germany, Iceland, France and the US, amongst other places, in large-scale data analysis. The finance sector has also hired several graduates recently.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and our Master's programmes have some of the highest employment rates and starting salaries.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

This MSc is one of the few leading Master's programmes entirely dedicated to machine learning. It combines a rigorous theoretical academic framework along with specific knowledge of a variety of application fields to fast-track your commercial career or to prepare for PhD research.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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The master of science in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Read more

Program overview

The master of science in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Professionals in these fields use their strengths in business, modeling, and data analysis to understand and use complex financial models, often involving differential and stochastic calculus.

The program addresses a vital and growing career field, reaching beyond banking and finance. Typical job titles include risk analyst, research associate, quantitative analyst, quantitative structured credit analyst, credit risk analyst, quantitative investment analyst, quantitative strategist, data analyst, senior data analyst, fixed income quantitative analyst, and financial engineer. Computational finance is an excellent career option for technically-oriented professionals in the fields of business, math, engineering, economics, statistics, and computer science. Programming knowledge is highly preferred.

Plan of study

The curriculum offers an integration of finance, mathematics, and computing. The required mathematics courses have substantial financial content and the experiential computational finance course, which students take during the summer, makes use of skills learned in the mathematics, analytics, and finance courses taken up to that point. The program has a strong multidisciplinary nature and combines the expertise of four of RIT's colleges. The program is a full-time, 17-month curriculum beginning exclusively in the fall. The program ends with a required non-credit comprehensive exam based on the courses completed by the student.

Curriculum

Computational finance, MS degree, typical course sequence:
-Accounting for Decision Makers
-Survey of Finance
-Equity Analysis
-Debt Analysis
-Advanced Derivatives
-Mathematics for Finance I
-Mathematics for Finance II
-Analytics Electives
-Electives
-Computational Finance Experience

Other admission requirements

-Submit official transcripts (in English) from all previously completed undergraduate and graduate course work.
-Submit the results of the Graduate Management Admission Test (GMAT) or Graduate Record Exam (GRE) (GMAT preferred).
-Submit a personal statement (Applicants should explain why their background, please indicate mathematical and programming knowledge, and interests make them suitable for the program).
-Submit a current resume, and complete a graduate application.
-International applicants whose native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 580 (paper-based) or 92 (Internet-based) are required. Scores from the International English Language Testing System (IELTS) will be accepted in place of the TOEFL exam. The minimum acceptable score is 7.0. The TOEFL or IELTS requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions. For additional information on the IELTS, visit http://www.ielts.org.
-Completed applications for admission should be on file in the Office of Graduate Enrollment Services at least four weeks prior to registration for the next academic semester for students from the United States, and up to 10 weeks prior for international students applying for student visas.
-Accepted students can defer enrollment for up to one year. After one year, a new application must be submitted and will be re-evaluated based on the most current admission standards.

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IMF Business School, in collaboration with the Camilo José Cela University, launches the Master in Business Analytics and Big Data. Read more

IMF Business School, in collaboration with the Camilo José Cela University, launches the Master in Business Analytics and Big Data. This program aims to provide students with a global view of Big Data technologies and their use, as well as applied and practical training in Business Analytics. IMF is a member of the Association of Computer Technicians (ATI).

This Master's degree is aimed at both new graduates and experienced professionals who wish to focus on the new professions related to data analysis (Data Analyst, Data Scientists, Chief Data Officer, Data Engineer ...). The recommended access profiles are those related to ICTs, careers with a high qualitative component, and careers in business and economics.

The Master's Degree in Big Data of IMF, of an academic year of duration, is taught in online mode supported by an advanced technological platform that allows the student to access the study, regardless of geographical location or time availability.

With the IMF Student Centered methodology, the student is placed at the center of all training services and guides the institution towards academic and professional success. The student will be able to know his progress at all times, be attended when he needs to, access to all resources with total freedom and have a coaching service, headhunting and job placement.

All students who successfully complete this program will obtain a double Master's degree from the University Camilo José Cela and Master by MFI Business School. They will have at their disposal all the advantages of MFI:

  • Program Scholarships and Study Grants
  • Recruiters with over 12,000 vacancies posted in the last year
  • Unlimited tutoring
  • Live online classes
  • Masterclasses and networking sessions
  • Financing up to 12 months without interest or bank intervention
  • Virtual library with over 30,000 references
  • Virtual library with access to any master classes
  • Access to the VIP Club with discounts on leisure, travel, restaurants ...

TEMARY:

  • MODULE I - Technological Foundations for Data Processing
  • MODULE II - Models and Statistical Learning
  • MODULE III - Applied Automatic Learning
  • MODULE IV - Text Mining and Natural Language Processing (NLP)
  • MODULE V - Business Intelligence and Visualization
  • MODULE VI - Big Data Infrastructure
  • MODULE VII - Storage and Data Integration
  • MODULE VIII - Value and Context of Big Data Analytics
  • MODULE IX - Analytical Applications
  • MODULE X - Final Master's Work
  • COURSE I - English Course


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Why this course?. This masters degree programme will allow you to select leading classes that span the breadth of both computer and information sciences, including theoretical computer science, human-computer interaction, information sciences, software engineering, machine learning and big data. Read more

Why this course?

This masters degree programme will allow you to select leading classes that span the breadth of both computer and information sciences, including theoretical computer science, human-computer interaction, information sciences, software engineering, machine learning and big data.

You’ll study

You'll gain an understanding of the new challenges posed by the advent of the big data revolution, particularly in relation to its modelling, storage, and access. You'll also come to understand the key algorithms and techniques embodied within data analytics solutions, and be exposed to a number of different big data technologies and techniques, seeing how they can achieve efficiency and scalability, while also addressing design trade-offs and their impacts.

You'll learn key technologies that are at the heart of big data analytics such as NoSQL databases and Hadoop and the Map-Reduce programming paradigm. You will also be equipped with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply these.

You will also have the opportunity to implement and experiment with these machine learning algorithms using the most popular languages such as R and Python, and explore their applications to areas as diverse as analysing activity-related data captured using a smartphone to financial time-series prediction.

Individual project/dissertation

You’ll take on an individual research project on an approved topic related to your selected pathway. You’ll pursue a specific interest in further depth, giving scope for original thought, research and technical presentation of complex ideas.

Learning & teaching

Teaching methods include lectures, tutorials and practical laboratories. Dissertation is by supervision.

You’ll also have the opportunity to meet industry employers and participate in recruitment events.

Careers

Opportunities for graduates of the MSc Advanced Computer Science with Big Data exist in industries ranging from finance, films and games, pharmaceuticals, healthcare, consumer products and public services to dedicated IT organisations.

Future career options will include:

  • big data analyst
  • software engineer
  • big data
  • software engineer
  • data scientist
  • data consultant


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Walden’s online master’s in information technology program is designed to help you progressively build a 21st-century IT career—from the foundational skills required to enter the field to the specialized expertise needed for senior-level IT positions. Read more

Walden’s online master’s in information technology program is designed to help you progressively build a 21st-century IT career—from the foundational skills required to enter the field to the specialized expertise needed for senior-level IT positions.

Designed with input from IT industry leaders and employers, the online master’s in IT program offers core courses to help you develop essential programming, networking, and database management and development skills. After completing these core courses, you can then pursue one of five specializations that provide the advanced training required for senior positions in the in-demand fields of health informatics, cyber security, big data analytics, information systems, or software engineering.

Throughout the IT master’s program, virtual labs, hands-on applications, and the use of real-time analytics and business intelligence platforms help you gain the practical, real-world skills demanded by today’s employers.

Recently redesigned to help you gain highly marketable skills more quickly and affordably, the online master’s in information technology program enables you to earn a Graduate Certificate in Information Systems upon completing your first four courses.*

Center of Excellence

 Walden has been recognized as a National Center of Academic Excellence in Cyber Defense Education.

Learning Outcomes

Graduates of this program will be prepared to:

  1. Apply core information technology principles and practices.
  2. Apply best software engineering principles and practices to develop and maintain stable, secure, scalable, and maintainable software.
  3. Work in geographically dispersed teams to produce effective solutions to complex information technology problems.
  4. Recommend appropriate information technology solutions based on organizational needs and an evaluation of alternatives.
  5. Identify and discuss professional, individual, organizational, societal, and regulatory implications of information systems and technology.
  6. Select technologies, policies, and procedures to assure the confidentiality, integrity, and availability of information and IT systems.

*To receive the Graduate Certificate in Information Systems, you must satisfactorily complete ITEC 6111, ITEC 6115, ITEC 6145, and ITEC 6030 and apply for the certificate, which is awarded at no additional cost.

Find detailed information for this program, including possible occupations, completion rate, program costs, and median student loan debt.

MS in Information Technology Degree Specializations

Walden’s MS in Information Technology program offers a variety of specializations to help you meet your personal and professional goals.

Career options

Rapid advancements in technologies, new business opportunities for leveraging technology applications, and the anticipated retirement of many senior-level information technology professionals are factors contributing to the growing demand for technology talent. Employment of computer and system administrators, computer system analysts, and computer information systems managers is expected to grow by 25% from 2008 to 2018, according to the Bureau of Labor Statistics.

The MS in Information Technology can help you prepare for senior-level or leadership positions in big data analytics, engineering, programming, security, and systems architecture, including positions in:

Big data analytics

  • Business analytics specialist
  • Data analyst
  • Data scientist
  • Data visualization developer
  • Information security analyst
  • IT analyst
  • IT manager
  • Network administrator
  • Project manager
  • Software developer
  • System architect
  • Web developer

Computer software engineering

  • Computer software engineer, applications
  • Computer software engineer, systems software
  • Database designer/architect

Information technology security

  • Information technology security analyst
  • Network security analyst
  • Information technology business analyst
  • Technology risk manager

Network/systems administration

  • Computer programmer
  • Database programmer
  • Application programmer
  • Systems programmer

Learn more about the career outlook for graduates with a MS in Information Technology



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This programme has been designed in collaboration with representatives from industry and local government to ensure that students possess knowledge and skills that are highly valued by employers. Read more

This programme has been designed in collaboration with representatives from industry and local government to ensure that students possess knowledge and skills that are highly valued by employers. The degree specifically addresses the need for graduates who have a good understanding of spatial data and the more technical aspects of Geographical Information Science and Systems (GIS) including: 

  • The role of spatial data and information systems in the context of the research literature and current industry practice
  • Expertise in spatial data collection
  • Management and analysis
  • High-level technical abilities such as programming and spatial data analytics.

Students will critically evaluate the role of spatial data and information systems in the context of the research literature and current industry practice. Students will be able to demonstrate expertise in spatial data collection, management and analysis with the use of specific GIS software such as QGIS and ArcGIS. High-level technical abilities such as programming and spatial data analytics will also be taught. The completion of an independent research project will allow students to showcase their organisational and management skills in addition to being able to critically evaluate and synthesize new and emerging concepts and techniques from a wide range of research literature.

Collaborations with local industry and government will allow students to develop interpersonal skills in addition to an understanding and experience of the relevant professional, legal, social and ethical frameworks that they will need to adhere to as professionals within the area of spatial data science.

Full time

Year 1

Students are required to study the following compulsory courses.

Students are required to choose 15 credits from this list of options.

Part time

Year 1

Students are required to study the following compulsory courses.

Year 2

Students are required to study the following compulsory courses.

Students are required to choose 15 credits from this list of options.

Assessment

Assessment for each course will be various forms of continuous assessment as described in the course specifications. The continuous assessment for each course will involve an appropriate combination of coursework, presentations, peer assessment, practical work, group work and log books.

Careers

Possible jobs for graduates could include GIS graduate consultant, spatial analyst, GIS project manager, GIS developer and data curator. Students could also go on to further research opportunities (e.g. a PhD).



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