How to be a better data scientist in 5 steps


Data science is a multidisciplinary field ranging from machine learning to mathematics. Here are five ways you can be a better data scientist.

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A data scientist works on new ways to capture and analyse data using a variety of scientific and technical methods.

Sitting on the intersection between science, maths and technology, the data science industry is constantly at the forefront of new discoveries in a whole variety of sectors.

Society relies on data scientists to provide it with scientifically backed insights and data to function better. (You can take “better” to mean anything from fairer to more efficiently, or even something in between.)

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 the two roles, such as a need for curiosity, a love of stats, creativity and problem solving.

A data scientist can work for governments, in the R&D sector, in academia, as well as in the private sector. For example, David Azcona got his PhD at Dublin City University before taking up his current role as a senior applied data scientist at fashion tech company Zalando. He works on the company’s marketing insights team.

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Data scientists’ work is often incredibly detailed. It involves 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.

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 programming, we think of software engineers and developers, but data science is heavily reliant on programming also. The most common ones for data scientists are R, Python, C++, Java, Hadoop, SQL, Tableau and Apache Spark.

As the universal data language, SQL should be your main focus after you master the basics of Python and R.

According to Hays’ senior regional director in Australia and New Zealand, Adam Shapley, it is also important to have an understanding of machine learning as the data science sector often overlaps.

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 stats to a large extent, but don’t neglect the wider, human aspect. It is an applied science, after all. If you learn to communicate your work in a way that’s easily understood by lay 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 like him are researchers “at heart.” Cohen spoke to last year about his role as chief data scientist at the US analytics company Anodot.

He said that truly “talented and resourceful” chief 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, like Cohen is, this skill is especially important as spending too much time on one aspect of a project can derail the whole thing leaving other aspects of a project rushed or unfinished. Don’t make a task that you can’t complete within your budget or timeframe.

As we’ve already established, data science can be daunting. You need to carefully plan what tasks you need to complete before each job so you don’t find yourself getting bogged down.

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