Why Is Data Science Better Than Doing Science?

This was one of the questions asked at our Cambridge talk on Friday. It is an excellent question. It got me thinking and I think the complexity of the answer deserves a small paragraph here.

For any comparisons, it makes sense to start by defining some criteria; in this case, let's assume 4 aspects that people often consider important: renumeration, technical merit, day-to-day lifestyle and career prospects.

In terms of renumeration, the average salary of a data scientist in the UK does not quite match those in the US (where the average is $123,000 and someone with two years experience can fetch between$200,000 to \$300,000). For the UK, the average is £60,000. It is likely that the UK salaries will move in the direction of the US, as companies here wake up to the importance of a data driven culture. Even without this, I think we can safely say that it is more than a postdoc salary, with a steeper gradient of increase with experience.

Of course, money is not the only consideration. For many of us, insatiable problem solvers, we want to be at the forefront of science and technology. In the case of data science, this frontier exists in both academic and commercial environments. many data science tools were first developed by industry: hadoop by Yahoo, mapReduce by Google. For many of the cutting edge techniques, the theoretical framework was developed in academia, but only industry could offer the resources to really put it into practice. Some people prefer working on theory, some prefer seeing their algorithm working in real life.

In terms of day-to-day lifestyle, data science offers the chance to work in small teams that move quickly. It tends to be highly collaborative, with everyone pitching in their expertise. This is in contrast to academia, where the pace tends to be slower. Again, neither of these is uniquely better, it just depends on which working style you prefer.

Lastly, the career prospects of data scientists. As a nascent field, there are a variety of opportunities available. In academia, it is relatively hard to be promoted (and getting harder). In data science, the inertia is not so large, and ambitious people with great technical expertise can make large strides in their careers.

The bottom line is that data science isn't for everyone. However, for academics who want to use their technical skills in a collaborative, fast-paced environment, it might just be a great option.

Thank you all who came to our Cambridge talk. The Cambridge crowd really rocks. We hope to see you again soon.