4 Essential Skills Every Data Scientist Needs

There’s more to data science than data. These 4 skills will help you land (and keep!) that dream job.
Headshot of author Sara A. Metwalli
Sara A. Metwalli
Expert Columnist
August 19, 2021
Updated: August 25, 2021
Headshot of author Sara A. Metwalli
Sara A. Metwalli
Expert Columnist
August 19, 2021
Updated: August 25, 2021

Whenever you look up a roadmap to becoming a data scientist, you’re often faced with a list of technical skills you need to develop and grow in order to start your career. However, most of these guides often miss—or purposefully neglect—soft skills that are equally as important as all your technical abilities.

A data scientist or developer is more than just a programmer or a data miner. They’re strategic business partners, project managers, science communicators, analysts, and idea generators all rolled into one.

Being a data scientist goes beyond writing models with high accuracy; beyond sitting in front of a computer, learning how to code, how to use a specific model, or read the latest research in the field.

4 Essential Skills for Every Data Scientist

  1. Clear and effective communication
  2. Basic business acumen
  3. Collaboration and teamwork
  4. The ability to maintain code

Let’s look at four essential skills crucial for any data scientist to have in their toolbox to excel in their career. Most of these are soft skills but we will look at one technical skill we often overlook.

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1. Clear and Effective Communication Skills

Communication is the most important skill that gets lost in most technical fields, not just data science. When your job is about dealing with complex concepts and making sense out of the world around us, we tend to overcomplicate everything.

There’s a reason for that! In most technical fields, when we hold meetings with other developers and programmers, or even researchers, we need to look well versed in the field.  As a result, we tend to perform by using technical jargon and complicated examples.

That strategy may work in a technical setting, but it will not work if you need to communicate your findings to a broader audience. The ability to communicate effectively goes beyond simply explaining complex ideas. To be an effective communicator you need to practice expressing your complex ideas thoughts using accessible language.

Moreover, an essential part of effective communication is using the correct visual aid that doesn’t contradict your language (or distract from it) but instead supports you and helps clearly communicate  your ideas.

To become a better communicator, practice the following:

  1. Get comfortable with both verbal and non-verbal communication. Body language says as much as the words you’re actually speaking. Practice presenting in a mirror or hop on a Zoom call with your friends and ask for feedback. 

  2. Keep things short and to the point. Try using the PIP model (purpose, importance, preview). State the reason for your work, why it matters and finally, how it works.

  3. Don’t depend solely on your visual aids. Graphs and charts are helpful but they can’t do your work for you. Explain what your visualizations represent (simply) as you’re presenting them.

  4. Time yourself! Most people tend to ramble when they’re speaking in public if they haven’t carefully planned out their presentation. If you have a clear timing breakdown for yourself, you can avoid that. It may seem silly, but even break down your points into small bits (point a=5 minutes; point b=3 minutes, etc.). Don’t forget to time yourself while you practice to make sure you’re staying on target.

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2. Basic Business Acumen

The first step of any data science project is data. You need to collect the data, clean it and then analyze it. To do that—to analyze the data and understand the story it’s trying to tell you—you need to have some basic understanding of the data source.

Most times, the data we work with is collected using a specific business model to serve a stated target. Understanding the basics of the business model you’re working within can help you better understand and analyze your data.

You don’t need to go to business school to better understand your data; you merely need to know the basics of the specific model followed in your workplace. What does your company do and what’s its value proposition? How does the organization earn revenue? Who are their target customers or audience?

Collecting and analyzing data without knowing the business model or company objectives is like putting together a piece of furniture without reading the instructions—or even knowing what you’re building for that matter! 

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3. Collaboration and Teamwork

As a data scientist, you never work alone; you’re always a part of a team working towards the same goal. Not only that but you’ll probably also need to interact with managers, designers, marketing professionals  and, most importantly, clients. 

To grow and prove yourself in any work environment, you need to be a good team player, stay open to listening to new ideas and always welcome constructive feedback. You’ll need to strike a balance between remaining open-minded and objective while maintaining your unique perspective (they hired you for a reason, after all!).

Another aspect of being a team player is collaborating with open-source projects. Open-source software is one way you can work on developing your skills, giving back to the community and getting to meet others who share the same interests as you.

Finally, a great way to be a team player is through mentorship. When you offer yourself as a mentor to newcomers—whether in the field at large, to a student or a work colleague—you prove you’re knowledgeable while demonstrating flexibility and generosity. 

 

4. The Ability to Maintain the Code

The final skill I want to talk about is a technical skill that we (myself included) overlook once when we get into the field: fluency in version control. 

Writing good code with a state-of-the-art model is not the end. In fact, when it comes to software development, there is no end; there’s always something to be added and improved. Writing an easily maintainable code is a skill that requires a lot of time and practice to master.

The first step is being comfortable with version control. I realize that version control is (at best) not fun, and in most instances, very confusing. Nevertheless, it's an essential skill that every data scientist should perfect.

If your code is well maintained, it will be easy for other data scientists to understand, build on and extend. If your data structure were to change, having a maintainable code will make the process of adapting to the new data much smoother.

Whether we like it or not, being a data scientist isn’t all about the data. Sometimes our work becomes  a mix of business, marketing and communication. In order to be an influential (and hireable!) data scientist, these are skills you’ll need to have in your tool belt. Believe me, these soft skills can transform your career and open up new opportunities for your future.

This article was originally published on Towards Data Science.

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