How to become a better data scientist in 5 steps

Data science is a multidisciplinary field that can cover everything from machine learning to mathematics. But, whatever your role, here are five ways to be a better data scientist.

A data scientist works on new ways to capture and analyze data using a variety of scientific and technical methods. Located at the intersection of science, math, and technology, data science is constantly at the forefront of new discoveries across a variety of industries.

The company relies on data scientists to provide science-based information 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 completed his PhD at the City University of Dublin before taking up his current role as a senior applied data scientist at the technology company Zalando mode, where he works in the company’s marketing analysis team.

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The work of data scientists is often incredibly detailed. It can involve extracting insights from structured and unstructured data, and applying that knowledge in a wide range of fields from industry and economics to science and human behavior.

It is important to note that data scientists are different from data analysts, whose job is to interpret the data provided to them. There are, however, crossovers between these roles, such as a need for curiosity, a love of statistics, creativity, and problem solving.

So, are you looking to up your game in this area? Here are five tips for becoming a better data scientist.

Maintain 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 technology used to collect data evolves. It’s important not to get overwhelmed by the fast pace of the industry and stay 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 development progress. Lean into your natural nervousness! Whether you want to improve your programming skills or brush up on a stats area, keep track of it. And, most importantly, keep learning.

Learn some programming skills

When we think of coding, we think of engineers and software developers. But data science also relies heavily on programming, and many data scientists need knowledge of R, Python, C++, Java, Hadoop, SQL, Tableau, and Apache Spark.

According to Hays’ Adam Shapley, understanding machine learning is also important because the data science industry often overlaps with this technology.

Be patient

We’ve all heard 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 appreciate a good puzzle will feel their limits tested by the workload required to tackle it.

Instead of getting frustrated or giving up, take a quick break. Come back to the problem later after a coffee or a walk. If you’re still stuck, ask a colleague for help. Often, a second set of eyes can do wonders for solving something you just didn’t spot.

Communicate your ideas

Yes, data science is largely focused on math and technology, but don’t overlook the broader human aspect. It is an applied science, after all. If you learn to communicate your work in a way that is easily understood by others, your value as a data scientist will be apparent to all.

Even participating in hackathons and attending events in your own industry can give you the confidence to talk about your work. We all know you get it, but can we?

Know your limits

Ira Cohen of Anodot said that data scientists are “researchers at heart”. Cohen spoke to last year about his role as chief data scientist at the US analytics firm.

He said truly “talented and resourceful” data scientists know when to leave the “rabbit hole” of research and get down to work. If you’re responsible for a team of people, this skill is especially important because 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 the tasks you need to complete in a project so you don’t get bogged down.

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Sean N. Ayres