Data science is a multidisciplinary field that can cover everything from machine learning to mathematics. But, no matter your role, here are five ways you can be a better data scientist.
A data scientist works on new ways to capture and analyse data using a variety of scientific and technical methods. Sitting at the intersection between science, maths and technology, data science is constantly at the forefront of new discoveries in a whole variety of sectors.
Society relies on data scientists to provide scientifically backed insights to inform and improve processes and services. A data scientist can work for governments, in R&D, academia or the private sector.
For example, earlier this week we spoke to David Azcona, who got his PhD at Dublin City University before taking up his current role as a senior applied data scientist at fashion tech company Zalando, where he works on the company’s marketing insights team.
Data scientists’ work is often incredibly detailed. It can involve extracting information from structured and unstructured data, and applying that knowledge across a broad range of areas from industry and economics to science and human behaviour.
It’s important to note that data scientists are different from data analysts, whose job it is to interpret the data they are given. There are, however, some crossovers between these roles, such as a need for curiosity, a love of stats, creativity and problem solving.
So, looking to up your game in this area? Here are five tips to become a better data scientist.
Keep a list of online learning resources and tools
Data science is a very broad field. Not only that, but it is constantly changing as the tech used to gather data evolves. It’s important not to let yourself get overwhelmed by the fast pace of the industry and to keep on top of your own learning goals.
As someone who is naturally curious about data and its impact, chances are you enjoy keeping lists and tracking your upskilling progress. Lean into your natural nerdiness! Whether you want to improve your programming skills or brush up on an area of statistics, keep track of it. And, more importantly, keep learning.
Learn some programming skills
When we think of coding, we think of software engineers and developers. But data science is heavily reliant on programming also, and many data scientists require knowledge of R, Python, C++, Java, Hadoop, SQL, Tableau and Apache Spark.
According to Adam Shapley of Hays, it is also important to have an understanding of machine learning as the data science sector often overlaps with this tech.
Be patient
We’ve all heard the saying that patience is a virtue, but it doesn’t come naturally to everyone. Working with complex data can be incredibly frustrating and even those who relish a good puzzle will feel their limits tested by the workload involved in getting to grips with it.
Instead of getting frustrated or giving up, take a quick breather. Come back to the problem later on after a coffee or a walk. If you’re still stuck, enlist the help of a colleague. Often, a second pair of eyes can work wonders in solving something you just hadn’t spotted.
Communicate your ideas
Yes, data science is focused on maths and tech to a large extent, but don’t neglect the wider, human aspect. This is an applied science, after all. If you learn to communicate your work in a way that’s easily understood by other people, your value as a data scientist will be obvious for all to see.
Even joining hackathons and attending events in your own industry can help give you the confidence to talk about your work. We all know you understand it, but can we?
Know your limits
Anodot’s Ira Cohen said that data scientists are “researchers at heart”. Cohen spoke to SiliconRepublic.com last year about his role as chief data scientist at the US analytics company.
He said that truly “talented and resourceful” data scientists know when to leave the research “rabbit hole” and get on with the task at hand. If you’re responsible for a team of people, this skill is especially important as spending too much time on one aspect of a project can derail the whole thing, leaving other aspects rushed or unfinished.
As we’ve already established, data science can be daunting. You need to carefully plan what tasks you need to complete in a project so you don’t find yourself getting bogged down.
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