How to improve your skills as a Data Scientist

13th May 2019


Generally speaking, knowledge cannot be stagnant. It has to be improved, challenged, and increased. In the absence of this intellectual ritual, the knowledge will vanish.

Intuitively, we all know this is an undeniable fact. 

Learning data science isn’t a cakewalk

Speaking of fields, data science is one of those fields where growth is crucial. In the IT world, 50% of the things you learn today will be outdated in 4 years . This most definitely applies to data scientists as well.

Being a data scientist is a swell job, they’re valued, in high demand and of course, the pay is awesome. Automatically, the process of becoming one will be hard. Don’t expect anything less!

Learning data science isn’t cakewalk, it takes a lot of energy, time and dedication from you. This is why thousands of aspiring data scientists never make it to the finish line.

If we are being truthful, you can’t actually rely on articles or courses that read “Master data science in 1 month”, they’re pure clickbait.

Getting into the field means making lots of mistakes, messing up previously built data pipelines, losing hours of work, getting discouraged and if you’re pumped, starting all over again.

It doesn’t just end there.

Once you’ve had real life experiences and applied your acquired knowledge to create business solutions, you now have to deal with staying relevant.

In other words, you now have to focus on improving your skills.

If you’re just starting out as a data scientist and wondering what skills to get your hands on first, this list will be of help.

  • Programming skills
  • Statistics
  • Linear algebra and calculus
  • Machine learning
  • Data visualization
  • Data wrangling
  • Business and Communications Skills
  • Data intuition
  • Software engineering

Being a data scientist isn’t just about learning data science, it’s more about improving your data science skills

Back to what we earlier emphasized on, 50% of the knowledge we acquire today will be outdated in 4 years – the world isn’t getting any slower.

To keep up with the trend, your skills need to be constantly sharpened. Keeping up with the pace means you have to:

  • Understand artificial neural networks
  • Improve your coding skills
  • Get better at communicating and proffering business solutions
  • Learn about AI
  • Learn about deep learning
  • Improve your mathematical/statistical skills
  • Improve your skills on data visualisation
  • Acquire more soft skills

Here are a few other skills that can also help you be a better data scientist:

  • DevOps
  • Tableau
  • Big data Hadoop
  • RPA tools (Robotic Process Automation)
  • AWS certification

You can’t stop with just acquiring more skills

All the skills you learned at the early stage will definitely help your career but you have to become more curious.

You need to ask questions that will be relevant 5-10 years from now, questions that will shape the ever-changing future of data science.

How many methods can I apply to this specific problem?

What makes this tool work this way?

What’s the logic behind its functionality?

Will it function in other similar tools? If it will, how?

If you can crack questions like these, changing between tools and programming languages will become easy.

From 2006 to 2016, popular data scientist designations have evolved from just “Data or Business Analyst” to include “Data Scientists, Business Analysts, Big Data Specialists, Machine Learning Specialists, and Data Visualization Experts”.

This is a clear representation of the changing tide that will keep occurring and will also become faster.

Big brands are getting more satisfaction from the technology and are implementing new machine learning and data science initiatives. This is why, even though you can’t possibly become an expert in 2-3 years, you still can’t stop exploring new areas.

You already know it’s hard, so you should also know that no matter how hard it gets, you must stay in touch with data science and machine learning communities and look out for new courses and online resources.

It’s not an option!

So, in order to secure your data science career in the future, don’t rely on the information you learned, instead, focus on improving your core skills.

Read also: Can a data scientist do the work of a data engineer?

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Oluwatobi Ogunrinde

A passionate writer hoping to educate people with her work. Oluwatobi enjoys writing about entrepreneurship and work culture. When not writing, you will find her reading about international politics.

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