Sample Certificate

The MSc has a total of 90 ECTS credits and comprises of 6 taught modules at 10 ECTS credits each and a Work Based Project at 30 ECTS credits.

Awards to be conferred

Master of Science in Big-Data Analytics

 

Aims

The aim of the Master of Science in Big-Data Analytics (MSBDA) is to train participants in how to uncover highly relevant data insights using advanced analytics and technologies. Program graduates will not only drive decision-making across companies but will also act as catalysts for the growth of companies.

This MSBD programme is a major section of information technology studies that focus on the business and financial analysis side of IT. While big-data studies usually only involve the organization and analysis of large amounts of data/information, programming and support skills may also be taught in programs of this type. Participants may also incorporate data collection and processing into their studies while gaining work experience.

Participants who choose to study big-data benefit by practicing and developing their critical thinking, management, and communication skills that will be vital to their success after graduation. Earning a master’s degree also increases earning potential and career opportunities.

Most big data graduates enter the IT field. Because of the nature of big data, a focus is placed on the analysis of data/information for a competitive business advantage, which means graduates are usually best suited for data analyst, management & decision maker positions. With additional business knowledge, it is possible to find work in other departments, especially in project management and development. To begin your studies in big data, find a program that meets your specific requirements.

The programme follows a work-based and problem-based learning approach, and participants will be expected to demonstrate how they have utilized their learning and development in solving real-life business-related decision issues based on ever-growing modern data and problems by integrating and building on perspectives from the course, the literature, case studies and hands-on their own contexts.

Programme Learning Outcomes

Upon successful completion of the MSc in Big-Data Analytics, the student will be able to:

  • Acquire the knowledge and skills of collecting and integrating data, modelling and advanced data analysis
  • Get the deep understanding of the core concepts, practice and software tools in the domain of data analytics.
  • Apply a deep understanding of relevant theory and practices predicting outcomes and create business solutions in a complex environment.
  • Design the decision support analytics report to aid managers in using analytics models
  • Communicate key issues effectively and creatively using numerical, graphical, oral and written skills.
  • Demonstrate knowledge of statistical data analysis techniques utilized in business decision making in various domains/ sectors.
  • Understand & target customers, take strategic decisions, cost optimization, improve customer experiences to solve real-life problems in the lifelong learning process.

Assessments

The underpinning principles which drive the assessment strategies adopted for this programme are the profile of the target students and the programme itself (its philosophy and associated learning outcomes). Assessment will normally be based on the candidate successfully demonstrating achievement of an appropriate combination of the following criteria which are aligned to the descriptors for Level 7 Master degree qualifications:

  1. a) A systematic understanding of a substantial body of knowledge, and a critical awareness of current big-data problems and/or new insights, much of which is at, or informed by, the forefront of the academic discipline, field of study, or area of professional practice;
  2. b) A comprehensive understanding of methods and techniques applicable to the practical work-based decision management issues which require solutions and improvements;
  3. c) Originality in the application of knowledge, together with a practical understanding of how to establish, identify and solve decision making problems using appropriate prediction techniques and tools.
  4. d) The ability to evaluate and criticise received analytics graphs, facts and figures.
  5. e) The ability to make reasoned judgements whilst understanding the limitations on judgements made in the absence of complete data; and choose alternate data for missing data.
  6. f) The ability to communicate the results of the programme of research as demonstrated in the style and overall presentation in a professional manner.

Entry Requirements

An applicant may be admitted on the basis of evidence to suggest that he/she will be able to fulfil and benefit from the objectives of the programme and achieve the standard required for the award

A number of criteria are used in considering admissions to the programme including candidates’ language proficiency, academic and professional qualifications. And includes the following:

  • A Bachelor’s Degree qualification in any subject from a recognised institution although an IT based degree is desirable or preferred.
  • A professional qualification equivalent to a degree and a minimum of two years of working experience.
  • Mature and high potential candidates without degree or equivalent qualifications but hold Diplomas or Advanced Diplomas with more than six years of work experience of which at least two years are at supervisory – managerial level with IT background.
  • Mature and high potential candidates without Diploma qualifications but with more than 8 years of work experience of which at least 3 years are at supervisory / managerial levels with IT background
  • Demonstrate English Language proficiency in order to participate in the programme taught in English

Advanced Standing/ Exemptions/ Credits Transfer (APL)
Consideration for the above for students admitted onto the programme may be considered either at the beginning of a programme, or beyond the beginning of a programme, through an assessment of that student’s prior learning, whether certificated or un-certificated.   The process for making such a decision is known as the Accreditation of Prior Learning (APL) is a matter of academic judgment exercised by the appointed panel considering applications and approvals of APL

Where cohorts of students are to be admitted with advanced standing on a regular basis, the arrangement should be subject to an Academic Progression Agreement.

Programme Structure

In designing this programme, the prior qualifications and corporate experiences of participants are taken into consideration in order to ensure a programme which builds on their prior knowledge and skills.

The MSc has a total of 90 ECTS credits and comprises of 6 taught modules at 10 ECTS credits each and a Project at 30 ECTS credits. The modules are:

  1. Analysing & Visualizing Data using Power BI or Analysing & Visualizing Data using Excel (optional – students to choose any one)
  2. NoSQL Data Solutions & Querying Data with Transact-SQL
  3. Data Warehousing and Processing Data with Azure Data Lake Analytics or Data Warehousing and Processing Data with Hadoop in Azure HDInsight (optional – students to choose any one)
  4. Real-Time Data Streams in Azure or Real-Time Data Analytics with Hadoop in Azure HDInsight (optional – students to choose any one)
  5. Arranging Data with Azure Data Factory
  6. Emerging Data Solutions with Azure Machine Learning or Analysing Data with R or Executing Predictive Analysis with Spark in Azure HDInsight (optional – students to choose any one)
  7. Plus a 30ECTS Project

The Project of between 8,000 and 10,000 words accrues 30 ECTS credits.

Mode of Delivery

Blended Delivery Mode

Self-Instructional Learning Material Face to Face Tutorials Online Discussions
Students are given a complete set of learning materials to facilitate independent study which can be downloaded from the designated Learning Portal. Face-to-Face classes conducted at a learning centre at 12 hours per module. Learners are encouraged to participate in online discussions with other learners and their tutors for at least 18 hours per module.

Fully Online Mode

Self-Instructional Learning Material Online Discussions
Students are given a complete set of learning materials to facilitate independent study which can be downloaded from the designated Learning Portal. Learners are encouraged to participate in online discussions with other learners and their tutors for at least 18 hours per module.

Location

For the Blended Mode and Face to Face Fully Taught Mode please Contact Us to find an Approved Learning Centre near you.

For the Fully Online Mode please enrol now to sign up for the next available intake.

Notional Hours

Notional hours are defined in terms of the amount of time it should take a learner to achieve the learning outcomes.  Each ECTS credit requires on average 20 notional hours of a learner’s time.

Guide to Learning Hours / Student Learning Time
This Level 7 Programme accrues 90 ECTS credits spread over 6 modules, a Reflective Log and a Work Based Project or 1,800 notional hours in total.  The programme can be completed within 10 to 15 months.

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