MIT’s $75,000 Big Data finishing school (and its many rivals)

For most students, a top degree in a field such as computer science or maths ought to be a passport to a career perfectly in tune with the relentless digitisation of work.

For the 30 graduates taking up a new one-year course at MIT’s Sloan School of Management in September, it will be only the prelude to a spell in a Big Data finishing school.

This first cohort of students will pay $75,000 in tuition fees for their Master of Business Analytics degree, with classes ranging from “Data mining: Finding the Data and Models that Create Value” to “Applied Probability”.

They will be calculating that the qualification will sprinkle their CVs with extra stardust, attracting elite employers that are trying to find meaning in the increasing volumes of data that businesses are generating.

MIT Sloan, which announced the course this month, is confident that there will be no shortage of people willing to sign up, expecting that each place will attract 15-20 applicants who feel they have the requisite strength in maths, computer science and statistics.

“The return on investment we expect to be very high [for those who take the course],” says Jake Cohen, senior associate dean for MIT Sloan undergraduate and masters programmes.

The plan is to increase the annual intake of students to 60 by 2020. The course “really looks to address the major demand we see in the market”, Mr Cohen adds.

The bullishness is by no means confined to MIT, known for its tech expertise. Other schools — such as London’s Imperial College Business School, NYU Stern, USC Marshall School of Business and Melbourne Business School — offer specialist masters courses in business analytics.

It is not hard to understand why such courses are taking off. With more devices being connected to the internet, the amount of data that can be sifted by businesses is growing rapidly, as are the investments by companies seeking to keep pace with this trend.

Workers who can demonstrate that they have advanced data skills are in high demand. When LinkedIn analysed global recruitment activity on its site over the course of 2015, it ranked “statistical analysis and data mining” as the second-hottest set of skills, after expertise in cloud and distributed computing.

Last month, to take just one example, PwC, the professional services provider, said it would add more than 1,000 data scientists to its deals business over the next two years.

Added gilt on the geek CV

But is it really necessary for already-desirable elite graduates of Stem subjects — science, technology, engineering and mathematics — to delay entering the workforce to add another qualification (and another chunk of student debt that will have to be repaid)?

Mr Cohen says those signing up to the new MIT Sloan masters degree will gain an edge in the jobs market through exposure to its “world class faculty and cutting edge research”. Professors teaching the course include Erik Brynjolfsson, co-author of the influential book about the technological reinvention of work, The Second Machine Age .

He says there will be a heavy emphasis on the need to “connect insight to action”, partly through a 10-week summer project at a company working on a real data science problem.

Mark Kennedy, director of Imperial’s KPMG Centre for Advanced Business Analytics, says highly technical graduates can be “good at Stem stuff and terrible at explaining what they are doing to a business person”.

A course such as Imperial’s one-year MSc in business analytics, which costs £26,000 and is in its first year of operation, can be a “rounding out” for such people, he argues.

Mastery of machine learning — getting computers to improve through their experience — and data visualisation “doesn’t naturally translate into knowing how to formulate the right question for a senior business decision maker”, he adds.

But Stem hotshots do not have to give up a whole year to a Big Data finishing school.

The Data Incubator, a young US company, runs an eight-week fellowship for postgrads who are looking to enter industry, for instance. The fellowship — a boot-camp style conversion course — is paid for by employers who seek to scoop up the best talents for their own data-wrangling needs.

The business was set up by Michael Li, who has worked as a data scientist at Foursquare, a leisure app, and as a quantitative analyst in finance.

Mr Li suggests that for those who already have a strong statistical background, it may not make sense to take a long course in business analytics — “and sleep through 80 per cent of it”.

Meanwhile, regular MBA programmes can also have a strong business analytics component, although not in such depth as a standalone masters course.

Demystifying data for generalists

At a less technical level, the boom in Big Data courses is also being fed by the need for a special type of worker — those who can bridge the gap between the data analysts and the managers who are supposed to frame the questions that the process is supposed to answer.

Chris Bradshaw plans to be one of these people linking the generalists in suits with the specialists in T-shirts. After an undergraduate degree in chemical engineering, he worked in management consultancy for 15 years before signing up for the inaugural Imperial MSc in business analytics.

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The 39-year-old was drawn to the intellectual challenge of the emerging discipline after the rather less elegant slog of helping to implement large-scale IT projects. “It’s a massive growth area,” he says of business analytics.

While some of his peers on the course want to develop expert coding skills in Python or R, a statistical computing language, he is content to “understand what goes in and comes out”.

That chimes with the attitude of Soumitra Dutta, dean of Cornell University’s Johnson business school, who has a Big Data research background. Although his school has not as yet launched a standalone business analytics degree, he says its courses already have a strong quantitative bent.

The growing technological sophistication of the subject means that it is getting harder for even the most tech-savvy to stay at the cutting edge of data science, he adds, meaning that a bridging role is a good one for a generalist to target.

“On the business side, I don’t think the real value is learning how to start programming. What we need to be able to do is learn how to work the programmers.”