Data scientist skills range from data prep to storytelling
There isn’t one type of data scientist. Some started out as developers, others IT analysts, some are jack-of-all-trade data creatives and others are businesspeople turned data junkies.
That variety was on display at the TDWI Accelerate conference in Boston recently, where software developers, business analysts, IT pros, database administrators, an astrophysics major and even a lawyer converged to learn about data scientist skills, as well as the strategies they’ll need to work in that role — or at least to understand the growing number of people who do.
“If I’m going to work with the data science team, I need to know what they are talking about. I want to understand the concepts, and I want to be able to call BS from time to time,” said David Kleiman, manager of litigation product development for a major legal information provider.
Kleiman’s role in productization led him toward data science, as more and more clients want litigation analytics to be part of the e-discovery process to predict possible outcomes of cases. To get there, data scientists at the legal information company collect data used to predict litigation outcomes with a high level of accuracy: the judge, the law firm, the parties involved, the court location and many, many other variables. Kleiman explained that they need to consider if the data is clean, what’s not there, and they have to get it all in the right format for analysis.
Indeed, anyone involved in data science projects knows much of the work involves wrestling the data into submission. There’s so much data, in so many forms, from so many sources, but also, so much is missing. Data scientist, with a median salary of $110,000 and over 4,000 job openings as of January 2017, ranks as the No. 1 job in America by Glassdoor.com. “Ninety percent of data science is ETL [extract, transform and load] — the data wrangling aspects,” said Michael Li, founder and CEO of The Data Incubator, a data science training organization, during a presentation at TDWI Accelerate. “Focusing just on the analytics layer is not going to deliver the value and the insights you are looking for.
