Masters in Big Data (Online MSc)
Masters in Big Data
The Masters in Big Data is an advanced degree designed to teach students how to collect, manage and analyse big, fast moving data, either for science or commerce. The programme covers the technology of Big Data and the science of data analytics, and provides students with the platform to gain practical skills in big data technology, advanced analytics and industrial and scientific applications.
The Online Masters in Big Data aims to develop and enhance the student’s career in data science. Students will learn important maths and computing theory, advanced computational techniques, and gain skills in cutting-edge technology such as Python, Hadoop, NoSQL and Machine Learning.
Modules covered in the programme are assessed by an exam or an assignment, or a combination of both. However, the emphasis is on the course work. Students will graduate as a data scientist specialising in big data. They will be equipped with the skills and knowledge to make sense of large sets of unstructured and structured data, and provide insights quickly, which will enable companies to make accurate decisions in a shorter period of time.
Towards the end of the Masters in Big Data, students will apply the knowledge gained to create a final project using Big Data technology. Students are supported by staff members, right from choosing a specialist topic, during the in-depth analysis of the topic and its technology, all the way till completion of the project. The solution presented in the final project is a showcase of the student’s knowledge and skills, which they can present to future employers as well.
Benefits of an Online Masters in Big Data
The course material has been developed in partnership with global and local companies who employ data scientists. Students will gain the skills and knowledge to manage, structure and analyse Big Data efficiently. They will:
- better understand the issues of scalability of databases, data analysis, search and optimisation
- develop the ability to choose the right solution for any task involving big data, including databases, architectures and cloud services
- have an enhanced understanding of the analysis of big data, including methods to visualise and automatically learn from vast quantities of data
- develop programming skills to build solutions using big data technologies such as MapReduce and scripting for NoSQL
- gain the ability to write parallel algorithms for multi-processor execution
During their study, Online MSc in Big Data students are even presented the opportunity to interact with professionals working within the profession of data science. These include data scientists working in companies like MongoDB, SkyScanner and HSBC.
Along with having close links with Scottish Informatics and Computing Science Alliance (SICSA), the University is also a member of The Data Lab.
Career Path after an Online Masters in Big Data
Upon completion of the Online MSc in Big Data you’ll be able to work in a wide range of sectors, such as energy and utilities, financial services, digital technologies, public sector and healthcare. Graduates get employed in positions such as:
- Data Architect
- Data Analyst
- Chief Technology Officer (CTO)
- Business Analyst
- Data Scientist
Students who complete the Online MSc in Big Data can pursue a PhD
A minimum of a second class honours degree or equivalent in a numerate subject such as maths, computing, engineering or an analytic science. Applicants without these formal qualifications but with significant appropriate work experience are encouraged to apply.
English language Requirements
Due to disruption in English Language testing caused by COVID-19, we are accepting alternative English Language tests (including online English Language Tests).
If English is not your first language we may require one of following qualifications as evidence of English language skills, however please contact your Stafford Higher Education Consultant for more English language options.
- IELTS Indicator 6.0 with a minimum of 5.5 in each sub-skill
- Cambridge C1 Advanced (CAE) 169 overall with a minimum of 162 in each sub-skill
- Cambridge C2 Proficiency (CPE) 180 overall with a minimum of 162 in each sub-skill
- Pearson Test of English (Academic) 60 overall with a minimum of 59 in each sub-skill
- IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing
- IBT TOEFL Special Home Edition Test 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing
- Trinity ISE II Pass overall with a Pass in each sub-skill, ISE III Pass overall and in all sub-skills, ISE IV Pass overall and in all sub-skills
- Aptis (4 skills) CEFR B2 overall and B2 in all sub-skills
- Duolingo 95 overall with a minimum of 90 in all sub-skills
- LanguageCert International ESOL B2 Communicator – Pass with minimum 33 in each sub-skill
Personal & Professional Development
The MSc Data Science programme here at the University of Stirling addresses the acknowledged shortage of business leaders and managers with a detailed working knowledge of data analytics. The course gives you the opportunity to develop specialist skills, which will help close the gap. Your success within the field of Data Science is dependent on your ability to balance your technical skills with your ‘soft’ skills. The Personal and Professional Development (PPD) module on the Programme is designed to facilitate your self-awareness, communication, critical thinking, and team-working competences that are vital to becoming an effective and resilient manager within the field of Data Science. Throughout the module, students are encouraged to develop methods of critical reflection to continuously enhance and transform their professional competences. In doing so, they will develop strategies for managing their own learning/study needs and career progression.
Commercial and Scientific Applications
Students will be able to understand the nature of Data Science projects in industry and in academia. They will gain an appreciation of the advantage gained for companies and researchers from Data Science projects, and also an understanding of the difficulties and issues involved in creating such projects. They will be exposed to a wide variety of Data Science applications.
Statistics with R
The module provides an introduction to Statistics and allows students to understand the fundamentals of Statistics and demonstrates how to apply various techniques to real data using R. In addition, the module allows students to actively be involved in a research project, providing them with experience in planning and conducting quantitative research that they can also benefit from other modules involving quantitative analysis. After taking the module, students will be able to conduct quantitative empirical research projects using R and take more advanced modules/topics in quantitative methods.
Relational and non-Relational Databases
This course compares traditional relational databases with new NoSQL models and concentrates on the MongoDB document store.
- The traditional relational database model
- Comparison of relational databases to new NoSQL stores
- MongoDB, Cassandra, Neo4j use
- Replication and sharding
- MapReduce on databases
This module covers the analysis of structured and unstructured (text) data using a number of advanced machine learning methods. The module is geared towards teaching an understanding of the methods of processes of applying data analytics to real world problems.
This course will equip student with the some basic mathematical knowledge and problem solving skills.