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Masters Degrees (Data Analyst)

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

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

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

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

Career Opportunities

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

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

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

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

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

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Summary. Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. Read more

Summary

Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. This MSc Data Science aims to prepare students for a successful career as a data scientist or business analyst working in any profession where large amounts of data is collected, hence there is a need for skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation. These type of skills are typically in high demand in IT business, security and health sectors, intelligent transport, energy efficiency and the creative industries.

More generally data and analytics capabilities have developed rapidly in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Most companies, however, are not capturing the full potential value from data and analytics because they do not have the required expertise. Consequently, the MSc Data Science aims to address these challenges by providing a firm grounding in the core disciplines of data analytics and information processing, partnered with a broad appreciation of aspects of other disciplines where data science can form natural synergistic relationships.

Modules

  • Business intelligence
  • Machine learning and data modelling
  • Data science foundations
  • Big data and infrastructure
  • Statistical modelling and data mining
  • Masters project (research)

Academic profile

Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.

Career options

The key message from employability and work-related learning initiatives is that enhancing opportunities to develop work-related learning and employability enhances the learning of the subject being studied. We understand the importance of including real industrial and commercial contexts to our student's experience, so this MSc Data Science will pursue opportunities for industrially linked teaching material and student project work. In this regard, we will utilise our business and industry links to facilitate an industrially relevant student project. Such projects create valuable experiences for the student, and additionally, they can also help to build new and ongoing collaborations with departments and companies, with the potential to tap into funding streams designed for industry-academic research and development.

A recent statement from Ulster University’s Careers Office indicates that Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Data analysts can work in large companies such as the ‘big four’ consultancies or financial services firms, or consumer retail firms, small and medium sized businesses such as marketing agencies’ or the public sector.



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Do you enjoy problem solving? Are you looking for a demanding career with a salary to match? Southampton Solent University’s MSc Data Analytics Engineering programme teaches students to make sense of a world where every action and transaction we perform has some aspect of data attached to it. Read more

Do you enjoy problem solving? Are you looking for a demanding career with a salary to match? Southampton Solent University’s MSc Data Analytics Engineering programme teaches students to make sense of a world where every action and transaction we perform has some aspect of data attached to it. Data analysts use these data sets to make meaningful inferences that can support business decisions, governmental policy changes and system designs.

As a conversion course, this master’s degree is well suited to students from a wide range of academic backgrounds. The course will help you to develop sought-after skills within the technology and big data environment, fully preparing you for a range of careers after graduation.

The one-year master’s level conversion course is designed to prepare students from a range of academic backgrounds for work in data analytics engineering. Students are also able to tailor the course to their own personal career ambitions through a research project. Many use this piece of work to springboard the start of their career or a further research study.

Topic covered include databases, data management, web technologies, analysis and computing fundamentals. Students will also study academic research methods, which will then inform their final research project.

Students are also supported to gain a range of transferable skills throughout the course. These include project management, critical thinking, organisation and presentation skills. The professional issues and practise unit helps prepare students for the workplace by looking at the wider computing industry and the contexts in which big data can be used most effectively.

What does this course lead to?

Graduates from this course would be well-suited for a range of data analytics and data systems design roles. If you are interested in research, the course offers opportunities to continue on to PhD study.

Who is this course for?

This conversion master’s course is ideally suited to students from a number of academic backgrounds who have a strong interest in learning to code and transform data.

The course is also suited to those with extensive industry experience in this area, and who wish to gain an academic qualification.

What you will study

Core units with CATS points:

  • Research Methods (15)
  • Professional Issues and Practice (15)
  • Pilot Project (15)
  • Research Project (45)
  • Computer Fundamentals (15)
  • Data Management (15)
  • Web Technologies (15)
  • Databases (15)
  • Data visualisation (15)
  • Data Analysis (15)

Facilities

Students have access to high-spec computer labs and make use of the latest design and development programs.

Students will test applications in our new device laboratory - a special test area integrated within one of our existing software development spaces. This arrangement allows you to test your website designs and apps on real equipment, ensuring they perform as expected on the target platforms.

Your future

Suitable roles for graduates include:

  • Business Analyst
  • Project manager
  • Database manager
  • Digital marketer
  • Software developer
  • Web Developer
  • Application development
  • Project manager
  • Senior database analyst
  • Senior user experience analyst
  • Software architect
  • Network deployment specialist.

Industry links

Course content is developed with input from a variety of sources including an industrial liaison panel. This ensures that your studies include the latest technologies and working practices.

You’ll also have the chance to work directly with real-world companies on live briefs, events and projects, while regular BCS meetings hosted at the University help you to build professional connections and secure valuable work experience opportunities.



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The Data Science Program will prepare you to design and build data-driven systems for decision-making in the private or public sector. Read more

The Data Science Program will prepare you to design and build data-driven systems for decision-making in the private or public sector.

Program description

The digital revolution brings with it an explosion of data that carries significant potential value for businesses, science, and society.

As data becomes easily available as never before, so too does its volume grow, and extracting useful quantitative insights becomes more and more challenging. 

The Barcelona GSE Master in Data Science prepares its graduates to design and build data-driven systems for decision-making in the private or public sector, offering a thorough training in predictive, descriptive, and prescriptive analytics.

The curriculum will guide students from modeling and theory to computational practice and cutting edge tools, teaching skills that are in growing global demand. 

Data Science students will be armed with a solid knowledge of statistical and machine learning methods, optimization and computing, and the ability to spot, assess, and seize the opportunity of data-driven value creation.

Students will learn how to apply classroom examples using real data and answering concrete business questions from the perspectives of different industries. Through an independent master's project and the opportunity for industrial practicum work conducted with local businesses, students can have the opportunity to solve actual analytics problems hands-on.

Our courses are taught by leading academics and researchers in the fields of Economics, Operations, and Statistics, as well as experienced professionals from the analytics industry.The program also invites guest speakers and entrepreneurs working at the frontiers of the Data Science.

Degree

Upon successful completion of the program, students will receive a Master Degree in Data Science awarded jointly by Universitat Autònoma de Barcelona (UAB) and Universitat Pompeu Fabra (UPF). The degree requires the successful completion of 60 ECTS (European Credit Transfer System) credits of graduate courses (6 credits are equivalent to a 40 hour course), some compulsory and some elective. The students' final program must be discussed with and approved by the Master Director.

Who hires Data Science Graduates?

  • Consumer Goods, E-commerce, Entertainment, Pharmaceutical, and Telecommunications Industries
  • Logistics and Transportation Industries
  • Finance and Insurance Industry
  • Consulting and Research Organizations
  • Banking and Public Institutions

Examples of recent professional placements:

  • Accenture - Consultant (Barcelona)
  • Agoda - Data Scientist (Bangkok, Thailand)
  • Criteo - Business Intelligence Analyst (Barcelona)
  • Kernel Analytics - Data Scientist (Barcelona)
  • King - Junior Data Scientist (Barcelona)
  • Morgan Stanley - Quantitative Model Developer (Budapest, Hungary)
  • Nine Connections - Machine Learning Developer (Amsterdam, Netherlands)
  • Rhode Island Innovative Policy Lab - Data Scientist (Providence, RI, USA)
  • Stratagem Technologies - Reinforcement Learning Research (London, UK)
  • UNICEF - Monitoring and Evaluation Specialist (Kinshasa, Congo)




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This postgraduate Masters course is uniquely designed for both computing and non-computing graduates wishing to pursue a career in data science community. Read more
This postgraduate Masters course is uniquely designed for both computing and non-computing graduates wishing to pursue a career in data science community.

Data science is at the intersection of the fields of computer science, statistics, and design. In a rapidly evolving world, new types of big data are emerging from mobile devices, sensors, instruments, transactional systems, web logs, social media, the cloud and other sources. Businesses are accumulating big data at a rate that often exceeds their capacity to extract value from it.

MSc Data Science is ideally placed to provide you with technical knowledge and employer-focused skills required in a data scientist role. The course will develop your specialist knowledge of data acquisition, data cleansing, data analysis, information extraction, prediction, visualization, story-telling and explanation.

You'll gain hands-on experience using industry standard tools for data science, business intelligence and analytics including SAS, Tableau MS SQL Server, Oracle Database, and R Programming.

Modules

Research methods and professional issues
Future internet technologies
Statistical analysis and modelling
Business intelligence
Data management
Machine learning
Data mining and analysis
MSc thesis

All modules are assessed by a mix of coursework and examinations.

Teaching and learning

You'll make use of our e-learning suite and learn in a combination of lectures, seminars, workshops and private study. You'll have access to specialist software including Microsoft SQL Server 2012, Netbeans 7.x with Java 7, Oracle, Python, SAS and Visual Paradigm.

Placements

You are encouraged to actively seek placements, work experience and voluntary work during your studies to improve your CV and to give you the opportunity to put theory into practice. Many opportunities are offered through the University's central Employability team, who can support you in finding a placement.

Accreditation

In order to ensure the course runs in accordance with industry recognised standards we are seeking accreditation for both Chartered Engineer (CEng) status as well as Chartered IT Professional (CITP) accreditation by the British Computer Society, the Chartered Institute for Information Technology.

We are also seeking Chartered Engineer (CEng) status with both the Institute of Engineering and Technology and the Engineering Council.

Employability

Career opportunities range from IT services to business consultancy, and this course will prepare you for a career in the business intelligence community working actively with a range of resources including tools from major commercial vendors such as Microsoft and SAS.

As a graduate of this course you should to be able to work within the areas of business intelligence or business and data analytics, in roles such as a business intelligence specialist, data or business analyst or business intelligence developer.

LSBU Employability Services

LSBU is committed to supporting you develop your employability and succeed in getting a job after you have graduated. Your qualification will certainly help, but in a competitive market you also need to work on your employability, and on your career search. Our Employability Service will support you in developing your skills, finding a job, interview techniques, work experience or an internship, and will help you assess what you need to do to get the job you want at the end of your course. LSBU offers a comprehensive Employability Service, with a range of initiatives to complement your studies, including:

• Direct engagement from employers who come in to interview and talk to students
• Job Shop and on-campus recruitment agencies to help your job search
• Mentoring and work shadowing schemes.

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Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. Read more

Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. This Masters will provide you with a thorough grounding in state-of-the art methods for learning from data, both in terms of statistical modelling and computation. You will also gain practical hands-on experience in carrying out various data-driven analytical projects. Previous study of Statistics or Computing Science is not required.

Why this programme?

  • The University of Glasgow’s School of Mathematics and Statistics is ranked 3rd in Scotland and 11th in the UK (Complete University Guide 2017).
  • The Statistics Group at Glasgow is the largest Statistics group in Scotland and internationally renowned for its research excellence.

Programme Structure

Core courses

  • Bayesian Statistics
  • Big Data Analytics
  • Data Management and Analytics using SAS
  • Generalised Linear Models
  • Introduction to R Programming
  • Preliminary Mathematics for Statisticians 1
  • Probability 2
  • Regression Models2
  • Statistical Inference2

One Course is optional for students with sufficient background in Linear Algebra and Calculus.

Two students who have already completed an equivalent course can substitute this course by any other optional course, including optional courses offered as part of the MRes in Advanced Statistics (see the website for details).

In your project (60 credits) you will model data collected from research in environmental science, assessed by a dissertation.

Optional courses

Students choose at least two courses from group 1 and at least one course from group 2.

Group 1

  • Artificial Intelligence
  • Information Retrieval
  • Machine Learning
  • Programming

Group 2

  • Data Analysis
  • Professional Skills

Group 3

  • Biostatistics
  • Design of Experiments
  • Environmental Statistics
  • Financial Statistics
  • Functional Data Analysis
  • Multivariate Methods
  • Spatial Statistics
  • Stochastic Processes
  • Statistical Genetics
  • Time Series

 In your project (60 credits) you will tackle a complex data analytical problem or develop novel approaches to solving data analytical challenges. 

Career prospects

There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. Statistical Analysis and Data Mining was listed by LinkedIn as the hottest skill in 2014 and came second in 2015.

Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD. Our recent graduates have taken up positions as Statisticians with the Scottish Government, as Advanced Analytics Analyst at Deloitte Ireland, as Consultant at the World Bank and as Research Officer at Kenya Medical Research Institute (KEMRI).



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The Master of Science in Big Data Analytics for Business is a unique program that trains business professionals in the field of (online) marketing, finance, and operations. Read more

The Master of Science in Big Data Analytics for Business is a unique program that trains business professionals in the field of (online) marketing, finance, and operations.

Students are exposed to the leading-edge fundamentals in decision-making by extracting knowledge from Big Data, including social media data, customer web traffic data, Bloomberg’s financial data, and inventory process logs.

Students will learn to solve managerial problems by critically asking questions in the spirit of ‘What do we know?’ (Data driven) rather than ‘What do we think? (Gut feeling).

Program Advantages:

- Introduction of leading tools that convert data to knowledge

- Possibility to obtain business-relevant certificates

- Exposure to both academic and applied industry research

Career Opportunities:

- Digital/Web Analyst

- Customer Analyst

- Data Scientist

- Credit Risk Analyst

This program is under the process of being accredited with the Université Catholique de Lille as diplôme universitaire and with the Conférence des Grandes Ecoles.

Program

The Master of Science in Big Data Analytics for Business offers core modules in business, technology, and methodology as well as specialized modules in marketing, finance, and operations.

These modules will be covered over two semesters and the students will take their newfound knowledge and apply it in a professional environment during a 4 – 6 month internship.

Internship -

Students acquire real-life experience through a 4 – 6 month internship in France, or anywhere in the world.

The objective is to provide an opportunity where MSc in Big Data Analytics for Business students learn how to approach assignments and working relationships in a professional environment. They can apply their newfound knowledge in real world situations while receiving guidance and feedback from managers and colleagues.

New contacts made during their internships help create their professional networks.

French language classes -

French language lessons are mandatory for non-Francophone international students. Francophone students may choose German, Italian, Chinese, or Spanish.

Admission & Fees

The MSc in Big Data and Analytics for Business is for students with a bachelor’s degree with a quantitative component or business administration interested in a new and expanding field.

Admission requirements -

The program is open to candidates with a bachelor’s degree from a recognized university with good academic performance and a good command of English.

Native English speakers or students who have had two years of courses taught in English are exempt. A GMAT score is optional, not mandatory.

No prior knowledge of French is needed; however French language classes are mandatory for non-French speakers as part of the program.

Application process -

The application process is based on students’ online application available at https://application.ieseg.fr/ and review of the required documents.

Rolling admission is offered from October 2017.

Checklist requirements:

- Online application form

- Transcripts and diploma translated into English or French if necessary

- English proficiency test (IELTS 6.5 TOEFL IBT 85, TOEIC 800) if required

- CV / Resume

- Copy of passport

- 80€ application fee

Tuition 2017-2018:

- € 15,000 for domestic and international students

- International merit-based scholarships are available

Funding and scholarship-

IÉSEG has a merit-based International Scholarship Program with a tuition waiver of 15 to 50% per year. Selection is based on the applicant’s previous academic performance and overall application portfolio.

The scholarship application is automatic; students do not need to apply separately.

All international students are encouraged to check with Campus France and their own government to see if there are any scholarships available. For American students please check with Sallie Mae for private loan options.



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Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more

Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research dissertation/report (60 credits).

Core modules

  • Introduction to Statistical Data Science
  • Introduction to Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At least two from a choice of Statistical Science modules including:

  • Applied Bayesian Methods
  • Decision & Risk
  • Factorial Experimentation
  • Forecasting
  • Quantitative Modelling of Operational Risk and Insurance Analytics
  • Selected Topics in Statistics
  • Stochastic Methods in Finance I
  • Stochastic Methods in Finance II
  • Stochastic Systems

Up to two from a choice of Computer Science modules including:

  • Affective Computing and Human-Robot Interaction
  • Graphical Models
  • Statistical Natural Language Processing
  • Information Retrieval & Data Mining

Dissertation/report

All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

Further information on modules and degree structure is available on the department website: Data Science MSc

Careers

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study. 

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data 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: Statistical Science

82% 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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Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department. Read more

Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department. The MSc Crime Intelligence and Data Analytics (with Advanced Practice) course helps you develop the necessary skills to work in these sectors.

Course details

The work boundaries of the traditional police intelligence analyst and digital forensic investigator are becoming blurred – today’s analysts need to be cyber aware, understanding how communication records and web search histories can be extracted and analysed.

This course covers these areas as well as theories that provide a better sense of the causes of crime and the prevention measures that can be put in place to stabilise and reverse these trends. Analysts shouldn’t be phased by data simply because of its size, complexity or format. This course provides you with the skills to work effectively with large datasets, allowing you to make more informed decisions in relation to criminal investigations. Key features include writing code to quickly clean up data and packaging it so it’s suitable for analysis and visualisation. You will discover that the world constantly presents data in data frames or spreadsheets – our daily activities are invariably logged by a time, date, geolocation. You develop these skills along with your confidence in applying them to make more sense of the data – analysing Twitter downloads, searched words and images, geolocation points or big data. This course also explores strategies employed in forensic investigation. It gives you the space and opportunity to develop your own area of interest in a 60-credit research project where your supervisor enables you to maximise your skillsets from academic writing to data analytics.The two-year MSc Crime Intelligence and Data Analytics (with Advanced Practice) is an opportunity to enhance your qualification by spending one year completing an internship, research or study abroad experience. Although we can’t guarantee an internship, we can provide you with practical support and advice on how to find and secure your own internship position. A vocational internship is a great way to gain work experience and give your CV a competitive edge. Alternatively, a research internship develops your research and academic skills as you work as part of a research team in an academic setting – ideal if you are interested in a career in research or academia. A third option is to study abroad in an academic exchange with one of our partner universities. This option does incur additional costs such as travel and accommodation. You must also take responsibility for ensuring you have the appropriate visa to study outside the UK, where relevant.

What you study

For the MSc award you must successfully complete 120 credits of taught modules and a 60-credit master's research project.

Course structure

Core modules

  • Coding for Intelligence Analysts
  • Crime Science: Theories, Principles and Intelligence Sources
  • Cyber Security and Digital Investigation
  • Forensic Investigative Strategy
  • Legal Issues and Evidence Reporting
  • Research Methods and Proposal

Advanced Practice options

  • Research Internship
  • Study Abroad
  • Vocational Internship

Modules offered may vary.

Teaching

How you learn

You learn through a range of lectures, seminars, tutorials and IT laboratories, using a variety of software. Simulated problems and scenarios are posed in much the same way that analysts would face in the real world. You have the opportunity to use software that is found in real-world intelligence analysis and digital forensic units and data science. Engaging and learning from your peers will help you to achieve solutions. Much of the software you use in class can be downloaded for home use.

How you are assessed

You are assessed through a formal exam as well as through structured coursework.

Employability

You can expect to apply for an intelligence researcher and intelligence analyst role in a wide variety of career opportunities ranging from security, policing and business.



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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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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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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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Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department. Read more

Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department.

Course details

The work boundaries of the traditional police intelligence analyst and digital forensic investigator are becoming blurred – today’s analysts need to be cyber aware, understanding how communication records and web search histories can be extracted and analysed.

This course covers these areas as well as theories that provide a better sense of the causes of crime and the prevention measures that can be put in place to stabilise and reverse these trends. Analysts shouldn’t be phased by data simply because of its size, complexity or format. This course provides you with the skills to work effectively with large datasets, allowing you to make more informed decisions in relation to criminal investigations. Key features include writing code to quickly clean up data and packaging it so it’s suitable for analysis and visualisation. You will discover that the world constantly presents data in data frames or spreadsheets – our daily activities are invariably logged by a time, date, geolocation. You develop these skills along with your confidence in applying them to make more sense of the data – analysing Twitter downloads, searched words and images, geolocation points or big data. This course also explores strategies employed in forensic investigation. It gives you the space and opportunity to develop your own area of interest in a 60-credit research project where your supervisor enables you to maximise your skillsets from academic writing to data analytics.

What you study

For the PgDip award you must successfully complete 120 credits of taught modules. For the MSc award you must successfully complete 120 credits of taught modules and a 60-credit master's research project.

Course structure

PgDip and MSc core modules

  • Coding for Intelligence Analysts
  • Crime Science: Theories, Principles and Intelligence Sources
  • Cyber Security and Digital Investigation
  • Forensic Investigative Strategy
  • Legal Issues and Evidence Reporting
  • Research Methods and Proposal

MSc only

  • Research Project

Modules offered may vary.

Teaching

How you learn

You learn through a range of lectures, seminars, tutorials and IT laboratories using a variety of software. Simulated problems and scenarios are posed in much the same way that analysts would face in the real world. You can expect to use software that is found in real-world intelligence analysis/digital forensic units and data science. An element of the learning is through peer engagement, learning from others to achieve solutions. Much of the software you use in class can be downloaded for home use.

How you are assessed

You are assessed in formal examination settings as well as through structured coursework.

Employability

Career opportunities

You could expect to apply for intelligence researcher and intelligence analyst roles in a wide variety of career opportunities ranging from security, policing and business.



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This intensive programme in data science and software engineering is designed for graduates who are new to computer science and provides an excellent grounding for working as a data scientist or analyst in industry. Read more
This intensive programme in data science and software engineering is designed for graduates who are new to computer science and provides an excellent grounding for working as a data scientist or analyst in industry. You will gain a broad knowledge of computing and acquire programming and data analysis skills, as well as comprehensive, practical problem-solving and analytical skills. You will also critically explore current research and methodologies and have the opportunity to investigate an area of current research in more depth via a project.

If you are new to computer science, this programme provides a solid foundation for a career in IT as a data scientist or analyst. For those already working in IT, the programme is an ideal opportunity to strengthen and update your knowledge and skills in the areas of data science and software engineering, while obtaining a formal Master's qualification.

This programme has been funded by the Higher Education Funding Council for England (HEFCE), as part of an innovative initiative to fund conversion courses in computing and engineering. This course uniquely enables students without any previous computer or data science experience at undergraduate level to study towards a Master's degree in this area of emerging importance. Crucially, the course covers both data science and software engineering, a combination of skills sought after in industry.

Why study this course at Birkbeck?

This programme is ideal if you are new to computer science and want to develop a career in IT as a data scientist or analyst.
Our Department of Computer Science and Information Systems is one of the longest-established in the world - we are celebrating our 60th anniversary in 2017.
We provide a stimulating teaching and research environment, with academic specialists in all fields, including information and knowledge management, web and pervasive technologies, computational intelligence, and information systems development, among others.
Our research dates back to the late 1940s, when one of the first electronic computers was developed at Birkbeck by Dr Andrew Booth. We now house the Computational Intelligence Research Group and the Information Management and Web Technologies Research Group, both of which collaborate with other research groups and with industry, in the UK and abroad, and undertake interdisciplinary research in the life, natural and social sciences, and the humanities.
We are also part of the London Knowledge Lab, a unique collaboration between Birkbeck and the UCL Institute of Education, which brings together computer and social scientists to explore how we learn, the role of technology in this process, and how technology relates to broader social, economic and cultural factors.
In the 2014 Research Excellence Framework (REF), more than 75% of our research outputs in Computer Science were ranked world-leading or internationally excellent.
You will have 24-hour access to several laboratories of networked PCs with a range of language compilers, database and other application software. We are connected, via the SuperJANET network, to the computers of other academic institutions in London, elsewhere in the UK and abroad.

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Join this unique qualification to become a leading, sought-after analyst, able to understand and communicate big data in social policy-related contexts. Read more

Join this unique qualification to become a leading, sought-after analyst, able to understand and communicate big data in social policy-related contexts.

Find out more about the Master of Analytics parent structure.

The Master of Analytics with a major in public policy is for public servants who want to be able to better understand, interpret and analyse large data sets. 

The degree equips you with the technical ability and critical thinking needed to transform complex data into information that can be used as evidence for making policy.

Massey University is the only New Zealand university that offers the Master of Analytics (Public Policy).

What will you learn?

The MAnalyt teaches fundamental data analysis tools, including data mining, statistics and econometrics in the core courses. You will then learn how these tools are applied in the public sector.

The Master of Analytics (Public Policy) teaches a critical appreciation of the complexity of data analytics including an examination of cross-cultural data analysis; methodology; ethics; systems mapping; attribution and data management.

It also introduces case studies examining the roles and possibilities of geospatial data and the use of outputs from the integrated data infrastructure (IDI). The IDI is the New Zealand cross-agency data set used by government agencies and policy makers.

Real-world learning

At Massey University we ensure your learning is firmly based in a real-world context. To help you identify the most appropriate technique and data to address a problem, you need to understand the real challenges and context that policy makers face. 

The Master of Analytics will help you understand all these things, as well as give you training in communicating your findings in a compelling way. 

You will work in conjunction with an external agency on a real-world problem using the skills and knowledge you have already gained.

Based in Wellington

Block courses are held in Wellington.

Complete in a year

The qualification can be completed in 12 months of full-time study, or over a longer period of part-time study. The programme starts in semester one of each calendar year.

You need to complete three core statistics and analytics courses and three public policy specialisation courses. The programme ends with the applied or capstone project.

In demand by employers

The skills you will learn in the Master of Analytics meet the growing demand for graduates capable of processing, analysing and presenting data in ways that encourage informed and effective decision making. You will learn how to extract useful information from big data sets, while maintaining data security and privacy.

In 2011, Statistics New Zealand launched the IDI. It combines information from a range of organisations to provide the insights government needs to improve social and economic outcomes for New Zealanders. There are very few people skilled at using the IDI.

Why postgraduate study?

Postgraduate study is hard work but hugely rewarding and empowering. The Master of Analytics (Public Policy) will push you to produce your best creative, strategic and theoretical ideas. The workload replicates the high-pressure environment of senior workplace roles. Our experts are there to guide but if you have come from undergraduate study, you will find that postgraduate study demands more in-depth and independent study.

Not just more of the same

Postgraduate study is not just more of the same undergraduate study. It takes you to a new level in knowledge and expertise especially in planning and undertaking research.



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