Keys to the future: learning to unlock big data
Big data is often described as the new oil, the essential fuel powering the economy in the 21st century. But as every data scientist knows, data on its own is no fail-safe panacea for business woes. Nor is it a crystal ball, offering divination of the future. Big data needs to be processed intelligently to be applied successfully. A new module on the University of London’s MSc Data Science programme is teaching students how to do just that.
In October 2020, the MSc Data Science programme launched a new module on big data analysis. Dr Raju Chinthalapi, who teaches on the module, has been working with big data for his entire career. First as a quantitative researcher for Deutsche Bank, and then as a lecturer and researcher specialising in financial technology and the development of algorithms for better data analysis in finance.
Dr Chinthalapi likes the analogy of big data as the new oil. But he makes a crucial distinction.
“Big data is just crude oil,” he says. “It’s worthless until it’s processed.”
You could say that this module is all about how to use rigs- big data platforms- and refineries- machine learning algorithms- to extract value from that big data, including business insights and focused strategies.
Crucially, this programme will teach students how to bring computing to the data- and not to bring data to the computing. This saves both time and energy.
Data has obviously played a vital role in the rise of the tech goliaths like Google and Amazon, but enormous data sets, if handled well, also have the power to reveal patterns and trends that could help us solve some of the most pressing problems of business and society. Transformative techniques of analysis, prediction and enhancement are being rapidly adopted across the full spectrum of industries and businesses.
“There are many opportunities for businesses to interact with data today. We try to use available community hardware- ordinary laptops and PCs to create clusters for distributed computing. This way even small businesses without powerful computers can benefit. Crucially, this programme will teach students how to bring computing to the data- and not to bring data to the computing. This saves both time and energy.”
In a nutshell, Dr Chinthalapi says, this module is all about how to store data using distributed computing, how to bring computing to distributed data sets, and how to use machine learning for this distributed computing framework.
“We don’t know if there’s an oil well beneath the data until we interrogate it,” he says. “But what we can expect is that we will get some new perspective on our existing business. At a minimum, businesses can use big data to understand their own business better- like understanding their customers’ behaviour. If they are lucky, businesses could sell their data to others.”
Wherever we have digitisation, we have big data- and the recent Covid-19 crisis has forced rapid tech adoption across many industries. The possible break-up of big tech and changes in regulation around data ownership are likely to democratise the availability of data increasing the number of start-ups and demand for expertise in the field. Likewise, the trend towards a decentralised web fuelled by machine learning and natural language processing will enhance the possibilities for analysis even more. The skills-gap for data analysis is only growing.
Students will leave this module with practical experience of cutting-edge technologies. We have cluster computers so they can get hands-on experience with cluster computing. When they walk into their first job, they should be in a position to handle large data sets and extract value from them.
Dr Chinthalapi wants his students to be prepared for these changes by understanding the broad strokes of big data and the trends around it.
“Students will leave this module with practical experience of cutting-edge technologies. We have cluster computers so they can get hands-on experience with cluster computing. When they walk into their first job, they should be in a position to handle large data sets and extract value from them.”
The era of big data has arrived. This module is for students who are interested in being part of a new wave- a wave that is likely to last for the next 20 years or more, says Dr Chinthalapi. And it is not a future limited to data science students.
Data analysis involves interdisciplinary knowledge. Equipped with these tools, graduates can enhance the value they can bring to their own field- from biology to banking.
“We have a wide range of student backgrounds. They all have strong quantitative and analytical skills but not all of them are trained computer scientists,” Dr Chinthalapi says.
“We have people from economics, finance, maths, statistics, and engineering. It is a great personal as well as professional opportunity. Anyone can use this toolset in their domain.”
Find out more about the MSc Data Science programme.