Masters Degrees (Data Analyst)

This specialist Level 9 MSc in Big Data Management and Analytics aims to equip students with the necessary skills and analytic mind-set to pursue a career in a dynamic data analytics industry.

Why Study Big Data at Griffith College?

Designed specifically to address a growing need in the industry, the MSc in Big Data Management and Analytics at Griffith College is a 1-2 year programme which aims to build upon students' knowledge of computing science and create big data specialists. Delivered on a full and part-time basis, as a graduate of this course, you will:

• Obtain specialist knowledge and skills essential for a career in Big Data Management and Analytics.
• Establish an analytical mind-set necessary for independent academic and professional research.
• Gain a practical understanding of appropriate design and implementation strategies used in the development of Big Data solutions.
• Develop a team player attitude necessary to communicate problems, ideas and solutions to all levels of the industrial team.
• Build upon your knowledge of supporting topics in the area of Computing Science.

Course Highlights

• Emerging discipline with huge job opportunities
• Develop highly sought after skills
• Fully aligned with industry needs
• Access to innovative tools and technologies
• A dedicated experienced lecturing team

Course Content

This programme contains eight taught modules and a final Dissertation / Dissertation by Practice. Four modules are taught per semester; so learners complete eight taught modules over two semesters and then complete a project over a period of twelve weeks. The overall programme is one calendar year long if studied on a full-time basis and two years if studying on a part-time basis.

Modules Covered:

• Big Data Analytics
• Information Retrieval and Web Search
• Concurrent and Parallel Programming
• Cloud Computing
• Big Data Management
• Data Mining Algorithms and Techniques
• Applied Data Science
• Research Methods

Learners who successfully complete eight taught modules and do not wish to submit their dissertation may decide to exit with an award of Post Graduate Diploma in Big Data Management and Analytics (60 ECTS, level 9).

Academic Progression

On completion of the Level 9 MSc in Big Data Management and Analytics, students may progress onto a range of Level 10 Doctoral programmes on the National Qualifications Framework. The Postgraduate QQI validation means that your qualification is recognised not only in Ireland and Europe but throughout the world.

Career Progression

Through the MSc in Big Data Management and Analytics, you will have gained valuable professional experience, specialised in a key emerging field and developed many technical skills. There is a wide range of career options for our graduates including:

• Data Analytics
• Business Intelligence Analyst
• Big Data Solutions Lead Engineer
• Technical Product Manager
• Big Data Architect
• Data Analytics Consultant
• Video Analytics and Data Scientist
• Data Science Expert
• Multimedia Systems Developer
• IT Operations Manager

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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. 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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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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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 2016.

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 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. 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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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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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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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.

Degree information

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 from 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 Supervised Learning
-Statistical Design of Investigations
-Statistical Computing

Optional modules - st 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 comprising 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.

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:
-Towers Watson, Actuary Analyst
-Proctor & Gamble, Statistician
-Ernst & Young, Audit Associate
-Collinson Group, Insurance Analyst
-UCL, PhD Statistical Science

Employability
Data science professionals will be highly sought after as the integration of statistical and computational analytical tools becomes increasingly essential in all kinds of organisations and enterprises. A solid 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 go along with statistical expertise as graduate level. Data scientists should have a broad background so that they will be able to adapt themselves 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.

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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. 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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In the current knowledge economy and information age, the demand for social, market and marketing research has increased substantially as organizations have recognized the growing need for research on which key policy and strategic decisions are based. The Research Analyst graduate certificate program focuses on the theoretical, practical and ethical underpinnings of research, while also equipping you with the statistical, technical and professional skills necessary to do applied research in a variety of different settings throughout the public and private sectors.

In this program, you will focus on all of the major aspects of the research process including research design, information retrieval and evaluation, data collection, analysis and interpretation, and the preparation and presentation of the research findings.

Special features of the program include an emphasis on professional, ethical and legal implications when designing research studies; new research technologies for data collection; analysis; presentation; proposal writing; research entrepreneurship; project management; and program evaluation.

Professional Accreditations

This program is recognized by the Marketing Research and Intelligence Association (MRIA) as being an official post-graduate program designate. This enables graduates of the program to take advantage of Pathway 1 to the Certified Marketing Research Professional (CMRP) designation.

Course detail

Upon successful completion of the program, a graduate will:

• Retrieve, process, and present market research information and findings using current online and stand-alone information technology tools.
• Conduct social and market research with a high degree of accuracy and reliability, which can inform major organizational and business decisions.
• Write documentation to collect, and support the collection, of data for social and market research projects.
• Interpret qualitative and quantitative research through the analysis and presentation of empirical data to meet the needs and objectives of the client.
• Ensure that all analysis of numerical and text data is consistent with the appropriate principles of descriptive statistics and techniques of statistical inference.
• Design and implement research projects for international and domestic populations with varied cultural and linguistic demographic profiles.
• Formulate plans for social and market research that will meet the needs of the client and follow all theoretical, practical, ethical, and legal guidelines related to the collection of data and the privacy of personal information.
• Design and implement research projects that address the unique characteristics of public, private or not-for-profit organizations.
• Prepare applications for the research ethical review board in an effective manner.
• Complete all work in accordance with ethical, legislative, and professional requirements and standards.

[Modules]]

Semester 1
• RAPP 5001: Surveying Society
• RAPP 5002: Qualitative Research Methods 1
• RAPP 5003: Spreadsheet and Table Management
• RAPP 5004: Quantitative Research Methods 1
• RAPP 5005: Research Communication and Proposal Writing Skills
• RAPP 5006: Research Ethics and Standards

Semester 2
• RAPP 5011: Research Seminar
• RAPP 5012: Qualitative Research Methods 2
• RAPP 5013: Research Management
• RAPP 5014: Quantitative Research Methods 2
• RAPP 5015: Database Management
• RAPP 5016: New Research Technologies
• RAPP 5021: Research Analyst Placement

Work Placement

Students complete a twelve-week placement beginning in May, which allows them to gain invaluable practical and professional experience.

Your Career

Graduates of this program may find employment as analysts, community and social development officers, consumer advisers, economic policy researchers, education policy researchers, health and social policy development officers, health policy researchers, housing policy analysts, market and marketing researchers, program officers, research consultants, and social survey researchers.

How to apply

Click here to apply: http://humber.ca/admissions/how-apply.html

Funding

For information on funding, please use the following link: http://humber.ca/admissions/financial-aid.html

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The rapid growth of available data is transforming the way managers, accountants, investors or marketers are working. Including this data into their analyses, business plans and decisions is a must for firms to maintain their competitive advantage.

This programme is a response to these changes in the economic and technological environment. It aims to transform business students into multi-talented professionals, standing in-between their core field of expertise, such as management, finance, and marketing, and information technologies.

The programme mixes advanced modules in finance, marketing or management to provide a deep understanding of the most up-to-date methods for data-processing (machine learning, big data analysis) and data management. Our teaching philosophy is practice-oriented. With this in mind, after an intensive 12-month period of study you will complete your training with a six-month internship within a company.

Distinctive features of the programme are:
• Access to the Big Data Analytics and Technology Centre (BDATC)
• Strong focus on practical data processing and analysis skills
• Internship opportunities
• Industry links to local companies
• Cooperation with Xi’an Jiaotong University

Modules

• Introduction to Business Analytics
• Addressing Privacy and Ethical Risks of Data Sharing
• Econometrics
• Databases and Data Management
• Finance Pathway (I) - Financial Markets
• Marketing Pathway (I) - Social Media Marketing
• Management Pathway (I) - Strategic Business Analysis
• Data Mining and Machine Learning
• Social Network Analysis
• Big Data: Applications in Business
• Finance Pathway (II) - Portfolio Management
• Marketing Pathway (II) - Marketing Management
• Management Pathway (II) - Strategic Operation Management
• Internship Report / Dissertation

What are my career prospects?

Business analytics skills are in high demand in every sector of the economy, particularly within the fast-growing service sectors (finance, retail, marketing, ICT). Typical positions our graduates target include data or information analyst, technical consultant, IT-related project manager, and CRM analyst.

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Top archaeological researchers and heritage professionals use a raft of computational methods including GIS, data mining, web science, ABM, point-process modelling and network analysis. To impress employers you need the flexibility to learn on the job, leverage open data and program open source software. This MSc draws on UCL's unparalleled concentration of expertise to equip you for future research or significantly enhance your employability.

Degree information

Students learn about a wide range of concepts that underpin computational approaches to archaeology and human history. Students become proficient in the archaeological application of both commercial and open source GIS software and learn other practical skills such as programming, data-mining, advanced spatial analysis with R, and agent-based simulation.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), two optional modules (30 credits) and a research dissertation (90 credits).

Core modules
-Archaeological Data Science
-Complexity, Space and Human History

Optional modules
-Agent-based Modelling of Human History
-Exploratory Data Analysis in Archaeology
-GIS Approaches to Past Landscapes
-GIS in Archaeology and History
-Remote Sensing
-Spatial Statistics, Network Analysis and Human History
-The Archaeology of Complex Urban Sites: Analytical and Interpretative Technology
-Web and Mobile GIS (by arrangement with the UCL Department of Civil and Geomatic Engineering
-Other options available within the UCL Institute of Archaeology

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 15,000 words.

Teaching and learning
The programme is delivered through lectures, tutorials and practical sessions. Careful provision is made to facilitate remote access to software, tutorials, datasets and readings through a combination of dedicated websites and virtual learning environments. Assessment is through essays, practical components, project reports and portfolio, and the research dissertation.

Careers

Approximately one third of graduates of the programme have gone on to do PhDs at universities such as Cambridge, Leiden, McGill, Thessaloniki and Washington State. Of these, some continue to pursue GIS and/or spatial analysis techniques as a core research interest, while others use the skills and inferential rigour they acquired during their Master's as a platform for more wide-ranging doctoral research. Other graduates have gone to work in a range of archaeological and non-archaeological organisations worldwide. These include specialist careers in national governmental or heritage organisations, commercial archaeological units, planning departments, utility companies and consultancies.

Top career destinations for this degree:
-Database Administrator, Deloitte
-Data Science Analyst, M2M
-Graphical Information Systems (GIS) Technician, BSG Ecology

Employability
This degree offers a considerable range of transferable practical skills as well as instilling a more general inferential rigour which is attractive to almost any potential employer. Graduates will be comfortable with a wide range of web-based, database-led, statistical and cartographic tasks. They will be able to operate both commercial and oper source software, will be able to think clearly about both scientific and humanities-led issues, and will have a demonstrable track record of both individual research and group-based collaboration.

Why study this degree at UCL?

The teaching staff bring together a range and depth of expertise that enables students to develop specialisms including industry-standard and open-source GIS, advanced spatial and temporal statistics, computer simulation, geophysical prospection techniques and digital topographic survey.

Most practical classes are held in the institute's Archaeological Computing and GIS laboratory. This laboratory contains two Linux servers, ten powerful workstations running Microsoft Windows 7, a digitising table and map scanner.

Students benefit from the collaborations we have established with other institutions and GIS specialists in Canada, Germany, Italy and Greece together with several commercial archaeological units in the UK.

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

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

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

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

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

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

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

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

Our expert staff

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

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

Specialist facilities

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

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

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

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

Your future

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

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

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

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

Example structure

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

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

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The programme is designed to provide in-depth knowledge and skills within the field of computing. The course is aimed at students who already have a first degree in computing, have some existing software engineering skills, and wish to deepen their knowledge. This programme will have a strong focus on how data can be exploited within an organisation and will emphasise the communication of that data to a target audience.

Graduates would undertake a range of tasks associated with IT in organisations, and develop sophisticated solutions to IT problems.

Course Overview

The main themes of the programme are:
-Web based application development
-Database development, deployment and integration
-Project and team management in the computing sector

This programme will equip students with those skills at a high academic level and also crucially enable them to practically implement their knowledge because of the ‘hands-on’ emphasis of the programme.

Each of these themed areas is itself an area of significant international strategic importance and will enable students to gain important and valuable skills.

The Web based application development theme reviews current trends and technologies. Complex challenges faced by web developers are investigated in detail.

The Database development, deployment and integration theme covers the important areas of Data Warehousing and Data Analysis both of which are cited as important skills that are in great demand by businesses.

The final theme, Project and team management will concentrate on developing the skills of project management and systems analysis, both of which are in great demand by employers.

Modules

Part 1:
-Data Warehousing (20 credits)
-Distributed Web Apps (20 credits)
-Leadership and Management (20 credits)
-Managing Information Systems and Projects (20 credits)
-Research Methods and Data Analysis (20 credits)
-Web Technologies for e-Commerce (20 credits)

Part 2:
-Major Project (60 credits)

Key Features

This MSc provides significant technical content which will inform the management decision making process. In this context major organisations such as Tesco, Sainsbury and Amazon have been making significant investment into data warehousing and data mining technologies.

To effectively use this technology requires a large number of people to apply and manage the technology. The price of the technology has reduced significantly since the inception of data warehousing with Microsoft and Oracle supplying the appropriate add-on tools to their database management system products. These factors allow smaller organisations to gain a competitive advantage by utilising the large pool of transactional data that in some cases has been stored for many years.

In an industrial context, students may be required to manage teams of developers in small to large scale projects. To efficiently manage such projects, they will require a significant technical understanding of the issues arising to be able to appreciate the complexity of the tasks to be undertaken.

Indeed, in an SME this role is often fulfilled by a senior member of the development staff with both development and management duties. As either a developer or manager, the graduate would be expected to demonstrate their initiative and be able to use their research skills to rapidly adapt to the demands of new technology.

Assessment

Student works are assessed through combination of course works, lab based practical exams and written exam. The final mark for some modules may include one or more pieces of course work set and completed during the module. Project work is assessed by a written report and oral presentation. Part 2 of the MSc programme requires the student to research and prepare an individual project/dissertation of a substantial nature.

University students who are unable to successfully complete all aspects of the Part 1 may be eligible for a Postgraduate Diploma (120 credits) or Postgraduate Certificate (60 credits).

Career Opportunities

Students on this programme develop a broad range of technical skills and will study a number of topics related to information systems. The programme covers the three themes of Web based application development, Database development, deployment and integration, and Project and Team management in the computing sector.

A significant emphasis is placed on database management and the implementation of applications for manipulating information including both database systems and web applications. Additionally, graduates would be able to lead teams and manage projects.

It is expected that graduates would seek positions such as:
-Project manager (within the Computing field)
-Data analyst
-Database administrator
-Application developer
-Web developer

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This degree programme is unique through its dual emphasis on specialist training in the area Cognitive Neuroscience, as well as on generic, transferable research skills for career and professional development.

The course is based on a core curriculum of six taught modules and a research dissertation. The taught modules provide a detailed and critical understanding of contemporary research and analysis methods used in Cognitive Neuroscience, as well as generic professional development and research-related communication skills.

Students will acquire comprehensive research experience from working with researchers of international standing. The programme is especially recommended to graduates who want to:
-Specialise in the area of Cognitive Neuroscience
-Continue to do a PhD, or get a feel for research before embarking on a PhD
-Enhance their CV by a research-related Masters qualification

Modules and content:
-Introduction into Cognitive Neuroscience
-Mini-project in Cognitive Neuroscience
-Advanced Statistical Analysis
-Advanced Methods in Cognitive Neuroscience
-Advanced EEG and MEG Methods in Cognitive Neuroscience
-Communication skills in Research / Professional Development

In addition, students will complete a
-Research Dissertation

Learning, teaching & assessment

The course is delivered mainly through lectures, which are often followed up by laboratory-based practical sessions. The course is assessed by a combination of coursework, examinations, practical work, oral and written presentations, and the dissertation project.

Career prospects & Personal Development

This programme is designed to provide postgraduate-level education and training for graduates who want to specialise in the area of Cognitive Neuroscience, continue to do a PhD, or enhance their CV by a research-related Masters qualification.

Previous students on this programme have moved on to:
-PhD studies: destinations of recently successful PhD applicants include Aston, Cardiff, Derby, Liverpool, Sussex and Warwick
-Research-related roles: e.g. Research Assistant, Data Analyst (for example for the NHS)
-Education: e.g. Science Teacher, Educational Consultant

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