Data science can be used in a number of different ways, depending not just on the industry but on the business and its goals. But despite all the variety, a number of themes have emerged from these conversations. Here’s what they are:
What data scientists do. First, data scientists lay a solid data foundation in order to perform robust analytics.
Then they use online experiments, among other methods, to achieve sustainable growth. Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions. In other words, in tech, data science is about infrastructure, testing, machine learning for decision making, and data products.
The skills data scientists need are evolving. We posed the question, “Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” He made a case for the latter since communicating results remains a critical part of data work.
We’re also seeing increasing automation of a lot of data-science drudgery, such as data cleaning and data preparation. It has been a common trope that 80% of a data scientist’s valuable time is spent simply finding, cleaning, and organizing data, leaving only 20% to actually perform analysis. But this is unlikely to last.
One result of this rapid change is that the vast majority of my guests tell us that the key skills for data scientists are not the abilities to build and use deep-learning infrastructures. Instead they are the abilities to learn on the fly and to communicate well in order to answer business questions, explaining complex results to nontechnical stakeholders. Aspiring data scientists, then, should focus less on techniques than on questions. New techniques come and go, but critical thinking and quantitative, domain-specific skills will remain in demand.
Bowne-Anderson, H. (2018, August 15). What Data Scientists Really Do, According to 35 Data Scientists. Harvard Business Review.