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We offer a suite of Masters programmes at Stirling. This is a one year, full time taught MSc. designed to lead to a job in data science or analytics. Read more

Introduction

We offer a suite of Masters programmes at Stirling.
This is a one year, full time taught MSc. designed to lead to a job in data science or analytics.
Big Data skills are in high demand and they attract high salaries. The MSc Big Data at the University of Stirling is a taught advanced Master's degree covering the technology of Big Data and the science of data analytics.
The course is taught in the beautiful Stirling campus in the heart of Scotland with support from companies who recruit data scientists.
The course covers Big Data technology, advanced analytics and industrial and scientific applications. The syllabus includes:
- Mathematics for Big Data
- Python scripting
- Big Data theory and computing foundations
- Big databases and NoSQL
- Analytics, machine learning and data visualisation
- Optimisation and heuristics for big problems
- Hadoop and MapReduce
- Scientific and commercial applications
- Student projects

Key information

- Degree type: MSc
- Duration: One year
- Start date: September
- Course Director: Kevin Swingler

Course objectives

- An understanding of the issues of scalability of databases, data analysis, search and optimisation
- The ability to choose the right solution for a commercial task involving big data, including databases, architectures and cloud services
- An understanding of the analysis of big data including methods to visualise and automatically learn from vast quantities of data
- An appreciation of the size of search spaces in large problems and the ability to choose an appropriate heuristic to find a near optimal solution
- The programming skills to build simple solutions using big data technologies such as MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for multi processor execution.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS: 6.0 with 5.5 minimum in each skill
- Cambridge Certificate of Proficiency in English (CPE): Grade C
- Cambridge Certificate of Advanced English (CAE): Grade C
- Pearson Test of English (Academic): 54 with 51 in each component
- IBT TOEFL: 80 with no subtest less than 17

For more information go to English language requirements https://www.stir.ac.uk/study-in-the-uk/entry-requirements/english/

If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View the range of pre-sessional courses http://www.intohigher.com/uk/en-gb/our-centres/into-university-of-stirling/studying/our-courses/course-list/pre-sessional-english.aspx .

Structure and content

Our Big Data MSc is a mix of practical technology such as Hadoop, NoSQL, and Map-Reduce, important maths and computing theory, and advanced computational techniques. The course will teach you what you need to know to collect, manage and analyse big, fast moving data for science or commerce

REF2014

In REF2014 Stirling was placed 6th in Scotland and 45th in the UK with almost three quarters of research activity rated either world-leading or internationally excellent.

Strengths

Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The data lab with facilitate industry involvement and collaboration and provide funding and resources for students.
The Stirling MSc in Big Data has been developed in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Big Data projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The course features a long summer project, generally in partnership with a company or technology provider, that provides students with a showcase of their skills to take to employers or launch online.
We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Career opportunities

Demand for people with big data skills is projected to grow rapidly in the coming years. Average salaries are higher in Big Data jobs than the IT average and the skills shortage will make that gap bigger.
The Stirling Big Data MSc is run in partnership with industry and is designed to produce graduates with the skills that companies need.
e-Skills UK estimate that:
- The number of Big Data jobs in the UK rose by 41% from 2012 - 2013
- By 2020 there will be 56,000 Big Data jobs in the UK alone
- Big Data professionals earn on average 31% more than other IT professionals
- 77% of companies say it is difficult to recruit people with the Big Data skill they need

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Drawing on our research excellence in this area, this innovative programme of study in big data and business intelligence is designed to give graduates a competitive advantage in the modern, fast growing business domain. Read more

Drawing on our research excellence in this area, this innovative programme of study in big data and business intelligence is designed to give graduates a competitive advantage in the modern, fast growing business domain. This is one of the first MSc programmes in the UK covering these leading-edge technologies. The programme provides students with the deeper knowledge, advanced skills and understanding that will allow them to contribute to the development and design of big data systems as well as distributed/internet-enabled decision support application software systems, using appropriate technologies, architectures and techniques (e.g. data analytics, business intelligence, NoSQL, data mining, data warehousing, distributed data management and technologies, Hadoop, etc.).

Additionally, the programme enables students to understand and assess the security and legal implications of e-commerce applications and provides students with appropriate knowledge of business and commerce relevant to transacting business on the internet. The courses take a software engineering approach to the construction of applications and focus on modern software engineering methods, tools and techniques that enable an integrated life-cycle software development view.

Through our short course centre opportunity may also be provided to study for the following professional qualifications: Microsoft Technology Associate Exams; Certified Professional Java SE Programmer; Java Certified Associate; Oracle Certified Associate (OCA).

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.

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.

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

Assessment

Students are assessed through examinations, coursework and a project.

Professional recognition

This programme is accredited by the British Computer Society (BCS). On successful graduation from this degree, the student will have fulfilled the academic requirement for registration as a Chartered IT Professional (CITP) and partially fulfilled the education requirement for registration as a Chartered Engineer (CEng) or Chartered Scientist (CSci). For a full Chartered status there are additional requirements, including work experience. The programme also has accreditation from the European Quality Assurance Network for Informatics Education (EQANIE).

Careers

Graduates from this programme can pursue careers as data scientists, database designers and administrators, consultants, senior team members, programmers, analysts.



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The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. Read more
The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. This unique course will give you the skills you’ll need to succeed as a Data Engineer.

Why study Data Engineering at Dundee?

The role of “Data Scientist” has been described as the “sexiest job of the 21st Century. However, there is a emerging a new role, that of Data Engineer as more companies are realising they need employees with specific skills to handle the amount of data that is being generated and the coming tidal wave from the Internet of Things.

This MSc has been created with industry input to prepare its students with the skills to handle this wave of data and to be at the forefront of its exploitation. Students on the sister programmes (“Data Science” and “Business Intelligence”) have gone on to work for some of the biggest companies in the industry and we are confident that graduates from this MSc will have the same success.

The School of Computing at the University of Dundee has been successfully offering related MSc programmes such as Business Intelligence and Data Science since 2010. These innovative programmes attract around 40 students per year, drawn from across Europe and Overseas.

What's so good about Data Engineering at Dundee?

Our facilities:
You will have 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

Special features

The University of Dundee has close ties with the Big Data industry, including Teradata, Datastax and Microsoft. We have worked with SAS, Outplay, Tag, GFI Max, BrightSolid and BIPB, and our students have enjoyed guest lectures from Big Data users such as O2, Sainsbury’s, M&S and IBM.

You will be able to work with a range of leading researchers and tutors, including top vision and imaging researchers and BI experts. Our honorary staff include legal experts, entrepreneurs and renowned industry experts such as John Richards of the newly formed IBM Watson Group.

How you will be taught

The course will be taught by staff of the School of Computing. Depending on the modules you take this will include Andy Cobley, Professor Mark Whitehorn, and Professor Stephen McKenna.

What you will study

The course will be taught in 20 credit modules with a 60 credit dissertation. Students will require to complete 180 credits for the award of the MSc (including 60 credits for the dissertation). Students completing 120 credits (without the dissertation) will be eligible for a Postgraduate Diploma.

Course content

Each module on the course is designed to give the student the skills and understanding they need to succeed in the Data Engineering/ Science field. Content on the course includes (but is not limited to):

CAP theorem
Lamda Architecture
Cassandra, Neo4j and other nosql databases
The Storm distributed real time computation system
Hadoop, HDFS, MapReduce, and other Hadoop/SQL technologies
Spark and Shark frameworks
Data Engineering languages such as Python, erlang, R, Matlab
Vision systems, which are becoming increasingly important in data engineering for extracting features from large quantities of images such as from traffic, medical and industrial
RDBMS systems which will continue to play an important role in data handing and storage. You will be expected to research the history of RDMBS and delve in to the internals of modern systems
OLAP cubes and Business Intelligence systems, which can be the best and quickest way to extract information from data stores
Goals of machine learning and data mining
Clustering: K-means, mixture models, hierarchical
Dimensionality reduction and visualisation
Inference: Bayes, MCMC
Perceptrons, logistic regression, neural networks
Max-margin methods (SVMs)
Mining association rules
Bayesian networks

How you will be assessed

The course is assessed through a combination of examinations, coursework, presentations and interviews. Each module is different: for instance the Big data module has 40% coursework, consisting of Erlang programming and a presentation on nosql databases, along with an examination worth 60%.

Careers

Our experience suggests that graduates of this course will have most impact in the following areas:

Cloud and web based industries that handle large volumes of fast moving data that need to be stored, analysed and maintained. Examples include the publishing industry (paper, TV and internet), messaging services, data aggregators and advertising services

Internet of Things. A large amount of data is being generated by devices (robotic assembly lines, home power management, sensors etc.) all of which needs to be stored and analysed.

Health. The NHS (and others) are starting to store and analyse patient data on an unprecedented scale. The healthcare industry is also combining data sources from a large number of databases to improve patient well-being and health outcomes

Games industry. The games industry records an extraordinary amount of data about its customers' play activities, all of which needs to be stored and analysed. This course will equip students with the knowledge and skill to engage with the industry.

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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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Our MSc Advanced Computer Systems Development course is ideal if you are an Honours degree student, or equivalent, in a relevant discipline and would like to upgrade your software development skills and qualifications in line with new technologies and trends. Read more

Our MSc Advanced Computer Systems Development course is ideal if you are an Honours degree student, or equivalent, in a relevant discipline and would like to upgrade your software development skills and qualifications in line with new technologies and trends.

You will develop practical skills relevant to modern technologies for software systems development and management with different devices, enabling you to make an immediate contribution to an organisation’s IT functions.

Our course has significant industrial input to represent the latest developments in computer systems analysis, design and implementation – the main areas of employment in the computing/IT sector.

It uses various development tools and environments such as UML, Oracle, IBM Websphere, MS BizTalk, MS ASP.NET, NetBeans, Java Multi-Platform and Android SDK, MongoDB, data management/BI software such as MS Business Intelligence Development Studio, MS Project, and Security Architecture.

Our Advanced Computer Systems Development course is recognised by the British Computer Society as meeting the educational requirements for Chartered IT Professional membership.

"This course was the perfect choice for me – I wanted to have a degree in a computing field to develop a strong understanding and knowledge of software systems but did not want to opt for programmes with mandatory programming courses. The programme covers a wide range of topics, like software architectures, enterprise systems, databases, mobile technologies, project management etc, which provide various career path choices."

Ayesha Ahmed, Advanced Computer Systems Development graduate, now working as a Software Quality Assurance Analyst

Course Details

Our course uses various development tools and environments such as:

  • UML
  • Oracle
  • IBM Websphere
  • MS BizTalk
  • MS ASP.NET
  • NetBeans
  • Java Multi-Platform
  • Android SDK
  • MongoDB

Data management/BI software used includes:

  • MS Business Intelligence Development Studio
  • MS Project
  • Security Architecture

You will develop practical skills relevant to modern technologies for various software systems development and management with different devices, enabling you to make an immediate contribution to an organisation’s IT functions.

Teaching & Assessment

If you are a full-time student you will undertake three or four modules. If you are a student on a part-time basis then you will study two or three modules in each trimester. 60 credits are required for a Postgraduate Certificate award and 120 credits for a Postgraduate Diploma award. You will complete an individual MSc project (60 credits) to obtain 180 credits for a Master award.

Core modules that you will study include:

  • Ethics for the IT Professional
  • Managing Projects and Security
  • Research Design and Methods
  • Service Oriented Development

Optional modules (offered subject to demand) include:

  • Data Governance and Analytics
  • Database Applications for Business
  • Decision Support Systems
  • Enterprise Systems Development
  • Intelligent Systems
  • Interactive Design for Smart Devices
  • Mobile Business Technology and Design
  • Mobile Networks and Smartphone Applications
  • NoSQL Database
  • Oracle Database Development

Your knowledge and understanding is assessed through a combination of:

  • written examinations
  • assessed coursework
  • or coursework assignments only

Coursework assignments are also used to assess practical skills.

Career Prospects

Jobs

Upon graduation you will be equipped to make an immediate contribution to IT functions within organisations. You may enjoy a career with high-profile companies such as:

  • IBM
  • Oracle
  • JP Morgan
  • Bank of Scotland

Potential roles within these companies include:

  • Website manager
  • Database developer
  • Software developer
  • Business analyst
  • Doctoral (PhD) researcher

Further Study

You may wish to continue studying for a MPhil or PhD.



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Gartner defines Big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Read more

Gartner defines Big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. A recent IDC forecast shows that the market for Big Data technology and services will grow at a 26.4% compound annual growth rate through 2018, or about six times the growth rate of the overall information technology market.

Saint Mary’s new Master of Science in Computing & Data Analytics (MSc CDA) is a graduate-level, 16-month professional program designed to meet the complex challenges associated with Big Data. It combines two essential aspects of computing and data analytics:

- Software design, development, customization, and management

- Analytics and Business intelligence: the acquisition, storage, management, and analysis of huge amounts of data to improve efficiency, innovation, and decision making

The primary focus of the MSc CDA program is to develop highly qualified computing and data analytics professionals who will drive innovation and organizational success. MSc CDA prepares students for rewarding and lucrative careers in the data science industry through experiential learning opportunities and industry interaction.

Program Structure

In the first two semesters of the MSc CDA program, students are introduced to big data challenges and solutions through eight foundation courses:

- Software Development in Business Environment

- Statistics and its Applications in Business

- Human Computer Interaction

- Managing and Programming Databases

- Business Intelligence

- Managing Information Technology and Systems

- Data Mining

- Web, Mobile, and Cloud Application Development

The second half of the program features three applied learning choices:

- Applied Master Project - System and Functional Analysis / Implementation and Analysis of Results

- Internship

- Research Thesis

Visit https://smu.ca/academics/msccda-courses.html for course outlines

Benefits of the MSc CDA

- Develop in-demand skills and knowledge that lead to exceptional career opportunities

- Study with award winning instructors from Saint Mary’s Faculty of Science and the Sobey School of Business

- Interact with industry professionals through core courses, paid internships, sponsored projects, industry workshops, expert guest speakers, hackathons, and special events

- MSc CDA studnets create a rich portfolio of apps and software solutions from a wide variety of in-demand platforms (Java/J2EE, C#/.Net, JavaScript/jQuery/jQuery Mobile/node.js, HTML5, PHP, iOS, Android, IBM Bluemix, Azure, SAS, Cognos, SQL/MySQL, NoSQL/Mongo DB, R, Python, IBM Watson)

Why Saint Mary's University

Saint Mary’s approach to learning will uniquely nurture the promise you possess. With one-on-one access to award winning professors, who care about your academic performance and your future, you will have the kind of personal support that makes a difference. Saint Mary’s is located in the historic city of Halifax, the bustling economic and cultural centre of Nova Scotia on Canada's east coast.

Halifax is Atlantic Canada’s Innovation Hub - a leader in the information technology sector with a growing concentration of companies and research organizations focused on analytics innovation.



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This degree, offered by the Department of Computer Science, is aimed at graduates who already have a background in computer science or engineering and who wish to acquire the depth of knowledge and the skills required to help design, deploy and use the technologies through which systems can operate in networks and in a distributed way. Read more

This degree, offered by the Department of Computer Science, is aimed at graduates who already have a background in computer science or engineering and who wish to acquire the depth of knowledge and the skills required to help design, deploy and use the technologies through which systems can operate in networks and in a distributed way. You will be able to tailor your degree according to your interestm with optional modules available in cybersecurity, large-scale data storage and processing, and artificial intelligence.

Skills that you will acquire include the ability to:

  • analyse complex distributed systems in terms of their performance, reliability, and correctness
  • design and implement middleware services for reliable communication in unreliable networks
  • design and implement reliable data communication and storage solutions for wireless, sensor, and ad-hoc networks
  • work with open source and cloud tools for scalable data storage (DynamoDB) and coordination (Zookeeper)
  • design custom-built application-driven networking topologies
  • work with modern network management technologies (Software-Defined Networking) and standards (OpenFlow)
  • work with relational databases (SQL), NoSQL databases (MongoDb), as well as with Hadoop/Pig scripting
  • work with low-power wireless and mesh networking standards and technologies, such as IEEE 802.15.4, ZigBee and XBee
  • work with state-of-the-art microcontroller devices and kits, such as Arduino and Tessel, and miniature computing technologies, such as RaspberryPi

You will be taught by world-leading academics. We carry out research in all aspects of distributed computing and systems – including design and analysis of algorithms, large-scale and cloud-based systems, fault-tolerance, distributed storage, cloud computing, peer-to-peer, concurrency control, and multi-core computing – and in artificial intelligence, including cognitive and autonomous agents, automated planning, scheduling and domain-independent search control, and applications in surveillance operations, disaster response missions, space operations.

  • Study in a highly-regarded departments, ranked 11th in the UK for the quality of research publications (Research Excellence Framework 2014).
  • Benefit from strong industry ties, with close proximity to ‘England’s Silicon Valley’.
  • Graduate with a Masters degree with excellent graduate employability prospects.
  • Tailor your learning with a wide range of engaging optional modules.
  • Choose from a one-year programme structure or an optional year in industry.

Course structure

Core modules

  • Interconnected Devices
  • Advanced Distributed Systems
  • Wireless Sensor and Actuator Networks
  • Individual Project

Optional modules

In addition to these mandatory course units there are a number of optional course units available during your degree studies. The following is a selection of optional course units that are likely to be available. Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.

  • Introduction to Information Security
  • Data Analysis
  • Visualisation and Exploratory Analysis
  • Computation with Data
  • Semantic Web
  • Intelligent Agents and Multi-Agent Systems
  • Advanced Data Communications
  • Machine Learning
  • Large-Scale Data Storage and Processing
  • On-line Machine Learning
  • Network Security
  • Computer Security (Operating Systems)
  • Security Technologies
  • Security Testing - Theory and Practice
  • Smart Cards, RFIDs and Embedded Systems Security
  • Software Security
  • Introduction to Cryptography and Security Mechanisms

Teaching & assessment

Teaching is organized in terms of 11 weeks each. Examinations are taken in April/May of each academic year, except for Data Analysis for which the exam is in January. The individual project is taken over 12 weeks during the Summer.

A weekly seminar series runs in parallel with the academic programme, which includes talks by professionals in a variety of application areas as well as workshops that will train you to find a placement or a job and lead a successful career.

Assessment is carried out by a variety of methods including coursework, small group projects, and examinations, the proportions of which vary according to the nature of the modules.

This degree can be taken part-time.

Your future career

According to Cisco, the number of 'connected devices' (including vending machines, electricity meters and refrigerators as well as phones and computers) will exceed the number of people on the planet by a factor of two. By 2020 some 27 billion unique objects will be connected wirelessly to the Internet; from then on, the Internet of Things (IoT) will double in size every five years. By 2020, VisionMobile estimates that 4.5 million IoT developers will be needed.

We bring several companies to our campus throughout the year, both for fairs and for delivering advanced topics seminars, which are an excellent opportunity to learn about what they do and discuss possible placements or jobs.

Together with the Royal Holloway Careers and Employability Service, we offer you workshops and one-to-one coaching that train you to find a placement or a job and lead a successful career.



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IN BRIEF. Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area. Read more

IN BRIEF:

  • Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area.
  • Gain SAS certification.
  • Learn to tell a story from data. Become immersed in Big Data techniques and platforms, working with real-world messy data to gain experience across the data science stack.
  • Part-time study option
  • International students can apply

COURSE SUMMARY

Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?      

This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to    a  lack of suitably qualified people. This course is unique in the UK in that it has been developed as a MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem solving,    and  an ambition for a career in data science; then this course is for you.    

During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite  due  to    the  partnership of the University with the SAS Student Academy.  

"We are especially pleased to endorse the new MSc in Data Science. With the explosion of interest and investment in data science teams, our customers cannot get enough graduates with SAS-based analytical skills. Courses such as this new MSc are an important step forward by the University to addressing this skills shortage, especially amongst home students." - SAS

COURSE DETAILS

This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project worth 60 credits. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.

TEACHING

The course is focused around the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for examination. This makes a total of 300 hours total learning experience.

  • Lectures will be used to introduce ideas, and to stimulate group discussions.
  • Tutorials will be used to develop problem solving strategies and to provide practice and feedback with scenarios to help with exam preparation.
  • Workshops will be used to develop expertise in SAS tools, by analysing example datasets of increasing complexity.

ASSESSMENT

  • 50% of the assessment will comprise a practical project where students will be given some data, will devise and carry out an analysis strategy and will present their interpretations and explain their strategy. 
  • 50% will comprise an examination, which will assess more theoretical aspects of the course and will explore students’ immediate response to unseen scenarios or data.

CAREER PROSPECTS

A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”

With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.

FURTHER STUDY

The Informatics Research Centre in the School of Computing, Science and Engineering at the University of Salford builds on the history, success and achievements of the research in Computer Science and Information Systems developed at the University of Salford over the last thirty years.

Evolving around Data and Information in all their types and usages, the Centre covers all phases and processes from data pre-processing to engineering and visualisation. The Centre is developing novel methods and systems for the analysis and recognition of various data sets, learning behaviours and causal models. The techniques and systems developed have a wide range of potential applications including digitisation of historical documents, medical diagnosis, semantic tagging, segmentation of types of viewers and their behaviours, text mining and retrieval and data visualisation.

Forensic computing, digital investigation and Cyber security is another area of expertise supported by the centre both at the theoretical and application levels.

Many students go on to further research in the fields of:

  • Actionable Knowledge Discovery and Semantic Web
  • Software Engineering and applications
  • Big Data, Data Mining and Analytics
  • Image and document processing and analysis
  • Cyber Security and Forensics
  • Information visualisation and virtual environments

FACILITIES

Facilities include a new Dell Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialized in Machine Learning, Data Mining, Statistical Analysis and Big Data including:

  • R, SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner
  • NoSQL databases ie MongoDB


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The International Master's program in Data Analytics enables students to take up challenging research in the interdisciplinary field with major focus on data analytics. Read more

Course description

The International Master's program in Data Analytics enables students to take up challenging research in the interdisciplinary field with major focus on data analytics.
During the program you will learn deeply about bleeding edge models and methods in machine learning as well as about turning ideas into code and thus design, implement and run your own experiments. Training on a largescale compute cluster and big data applications is part of the program, too.
During your study project you will learn how to write research proposals, how to manage research projects as a team and how to present results to a critical.

The Data Analytics program combines complex mathematical and statistical models and methods from machine learning with problems from an application domain using tools and techniques for processing Big Data. The programs is structured in a way that it builds on the core data modeling and data analytics knowledge with application on the large volume of real-world data using modern big data technologies such as the Apache Hadoop, Apache Spark, NoSQL etc.

Core Modules

* Machine Learning
* Advanced Machine Learning
* Modern Optimization Techniques
* Programming machine Learning
* Big Data Analytics
* Distributed Data Analytics
* Planning and Optimal Control

Application and Admission

The program starts at University of Hildesheim in the beginning of October every year. For details on how to apply please visit our website http://www.ismll.uni-hildesheim.de/da

FAQs

The list of frequently asked questions can be found here http://www.ismll.uni-hildesheim.de/da/faq_en.html. We recommend you to go through these questions. If you didn't find your answer, please feel free to contact us.

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There is an enormous and increasing amount of data that is collected. Examples include not just traditional data such as sales transactions, but location data (GPS), interactions between people on social network, measurements of sleep patterns, medication being taken, state of health, and much much more. Read more

There is an enormous and increasing amount of data that is collected. Examples include not just traditional data such as sales transactions, but location data (GPS), interactions between people on social network, measurements of sleep patterns, medication being taken, state of health, and much much more.

A key challenge is then to make use of this wealth of data. How can we manage this data, and analyse it to exploit useful information that can guide decision making?

This emerging area goes under the name “Data Science”. There is growing demand for people, “Data Scientists”, who have the skills to manage and analyse enormous amounts of data using a range of techniques such as data mining, statistical techniques, and machine learning.

Data Scientist has been called the “Sexiest job of the 21st century”, and the unique combination of technical skills (stats, data management) and business understanding has been said to make Data Scientists “highly sought after and highly paid”.

Master of Business Data Science (MBusDataSc)

The MBusDataSc primary focus is to equip you to become a practitioner, allowing you to meet the needs of industry, and solve the data problems of the world. However, there will also be an alternative path that will focus on preparing students for research in the area (e.g. going on to do a masters by research or PhD).

The proposed degree is inherently multidisciplinary, featuring Information Science and Marketing, which gives the degree a strong business focus; as well as contributions from Computer Science and from Statistics.

Once you have completed the MBusDataSc you will have developed an advanced knowledge of data science. You will understand how data analysis can be used in business, including being able to identify opportunities to use data, be aware of ethical and privacy issues and possible mitigations, and be able to select appropriate means of presenting the results of analysis. You will be able to select and apply techniques to manage and analyse large collections of data.

Degree Structure

The programme of study shall consist of seven 20 point taught papers together with a 40 point applied project or research project. Papers are either taught in semester one, semester two or are full-year papers. 

You must complete:

INFO 424 - Adaptive Business Intelligence  

COSC 430 - Advanced Database Topics

INFO 411 - Machine Learning and Data Mining

MART 448 - Advanced Business Analytics  

INFO 420 - Statistical Techniques for Data Science

INFO 408 - Management of large scale data

BSNS 401 - The Environment of Business & Economics

Plus one of the following project papers

BSNS 501 - Applied Project  

or

BSNS 580 - Research Project (for students who may wish to progress to PhD study)

Graduate Profile

The University of Otago coursework masters programmes provide you with an opportunity to specialise in advanced study with a focus on either applied practical or academic research.

Graduates of the MBusDataSc will gain skills in three areas: those relating to the business and organisational context, those relating to computing technologies for managing data, and those relating to data analysis techniques.

As a graduate of the MBusDataSc you should be able to:

  1. Understand where data analysis is used in business
  2. Identify opportunities to use data to improve decision making in a business context
  3. Be aware of ethical and privacy implications, concerns, and approaches to mitigating these, including the ability to identify potential areas of concern, and recommend appropriate mitigation actions
  4. Use appropriate means of communicating the results of analysis in graphical form
  5. Develop and maintain databases, including familiarity with performance management for large databases
  6. Have knowledge of a range of data storage and manipulation technologies (such as relational databases, NoSQL, XML), and the ability to select an appropriate technology for a given context and need
  7. Use high performance computing tools (including cloud computing) to manage and analyse data
  8. Be familiar with a range of data analysis approaches (e.g. statistical techniques, data mining, data warehousing), and be able to to select and apply these techniques to suit the context
  9. Develop an appreciation of the concept of ethics from multicultural perspectives, including Māori development aspirations and also an international business environment


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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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USF’s one-year Master of Science in Analytics (MSAN) program delivers a rigorous curriculum focused on mathematical and computational techniques in the emerging field of data science. Read more
USF’s one-year Master of Science in Analytics (MSAN) program delivers a rigorous curriculum focused on mathematical and computational techniques in the emerging field of data science. The curriculum emphasizes the careful formulation of business problems, selecting effective analytical techniques to address those problems and communicating solutions in a clear and creative fashion.

98% of MSAN students are employed within three months of graduation at companies such as Google, Williams-Sonoma, Amazon, Capital One Labs, Eventbrite, and Mozilla.

A Technically Challenging Curriculum

The program's challenging curriculum features seven-week courses designed specifically for MSAN students — they're not offered in other programs or departments. Students master subjects from computer science, statistics, and management such as regression, web scraping, SQL and NoSQL database management, natural language processing, business communications, machine learning, cluster analysis, application development, and interviewing skills. Students primarily use programming languages like R and Python in their classes and learn how to effectively use distributed computing technology such as MapReduce, Hadoop, and Spark, and become intimately familiar with cloud technology such as Amazon Web Services.

Practicum Program

Practicum projects allow students to work an average of 15 hours per week for nine months tackling data science and analytics problems at companies around the San Francisco Bay Area and beyond. Past and current partners include Uber, Airbnb, Eventbrite, Google, Capital One Labs, AT&T Big Data, Zephyr Health, and the Houston Astros. Groups of two to four students - supervised by MSAN faculty - work on a data-driven business problem and produce a defined set of deliverables.

Faculty

Our faculty represent the fundamental multidisciplinary nature of the big data industry. They’re traditional academics and data scientists actively working in the field, using real industry experience to inspire their instruction. Their areas of expertise include deep learning, natural language processing, databases, statistical modeling, network analytics, algorithms, unsupervised learning, machine learning, optimization, health analytics and signal processing.

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