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Data Science and Fintech

Data Science and Fintech

The amount of digital data that exists doubles every year and is changing the way we live. At the same time, new technologies are emerging to organise and make sense of this new information.

Dr Kevin Swingler explains how Stirling’s data science and financial technology courses will equip you to play a vital role in the future of data.

Dr Kevin Swingler

Course Director MSc Big Data and MSc Financial Technology

Dr Kevin Swingler joined the University of Stirling as a research assistant in 1990, working on machine learning projects. In 1996, he co-founded Neural Innovation Ltd, a company that sold machine learning software and consultancy in the financial and marketing sectors. He has now returned to Stirling where he runs the MSc courses in Big Data and Financial Technology.

What are the differences between Big Data and Financial Technology?

“At Stirling, we take a data-driven approach to Financial Technology (Fintech), so the Big Data and Fintech courses share material such as data analytics. Big Data is more focused on technology - such as databases, scripting, machine learning and cluster computing - than Fintech, which has a focus on finance, banking and entrepreneurship. The computing part of the Fintech course is about cybersecurity, mobile apps and blockchain.”

There are four data science masters courses at Stirling. Which is for me?

“Fintech is the odd one out among these courses because it is geared towards a specific application in banking and finance. The other three courses are designed to give you more generic data science skills. Big Data concentrates on technology and skills such as Hadoop, Python, NoSQL and machine learning. Data Science for Business focuses less on the technology and more on the business impact of data science. Mathematics for Data Science is for students who have a love of maths and want an interesting and challenging subject that allows them to put their skills into practice.”

The Big Data and Fintech courses were built by Stirling academics. How did you create the course content?

“The courses were designed with one thing in mind: to prepare and qualify students for jobs in data science. We consulted with recruitment agencies and companies who hire data scientists and Fintech professionals, such as HSBC and MBN. Our goal was to give students the best CV that we could, which is why we do our best to find work placements for students during their summer project.

A great advantage of studying big data is the breadth of careers that are available after graduation. Our students have gone on to work in finance, marketing, health, sport, and local government, or in small start-up companies doing interesting things like computer vision and natural language processing.”

Stirling works closely with the Data Lab. What is it and what does this mean for me?

“The Data Lab is an internationally leading research and innovation centre in data science. Established with a £11.3 million grant from the Scottish Funding Council, The Data Lab enables industry, public sector and world-class university researchers to innovate and develop new data science capabilities in a collaborative environment. Its core mission is to generate significant economic, social and scientific value from data.

As a student, you’ll benefit from a wide range of learning and networking events that connect leading organisations seeking business analytics skills with students looking for exciting opportunities in this field.”

Do I need previous experience to study a data course? Can I go part-time?

“For Big Data, you will need a numerate degree and some experience of either statistics or computer programming. This can be gained from online courses and relevant work experience is also beneficial.

Often, our students arrive with a diverse range of skills from different sectors and backgrounds, and we enjoy the challenge of bringing everyone up to speed and progressing forward as a group.

Yes, all of our data science courses can be studied either full-time for one year or part-time for two.”

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