Key Data Science Skills Every Employer Is Looking For in 2021

Key Data Science Skills Every Employer Is Looking For in 2021

Want to do great in data science? Having these skills is a must to advance your career.

If you want to launch yourself in the world of data science in 2021 or are planning to scale up in your current role, here are the in-demand skills for a data scientist. This article will cover everything you need, from what programming languages you should know to what kind of software tools are gaining and losing importance.

According to many job postings for this field online, here's what employers are looking for in a data scientist.

1. Communication Skills

Data scientists are in constant, open communication with clients, so it's obvious that a data scientist needs to be quick and smooth in responding to client's requests, questions, and concerns when it comes to solutions and simple communication. Having a model that can solve the client's business conundrum is not enough, a data scientist needs to explain how it works and why it's making suggestions. It has been reported that only 11% of companies using artificial intelligence are getting a worthy ROI. The reason behind that is because the employees of the company don't know how it works and as a result, don't trust the recommendations or use the AI properly. This leads to extra costs that a company can't afford in the post-Covid world. For AI to work to its full potential, communication is a must for anyone in the field of data science.

2. Know The Top Programming Languages

In order of importance, we have:

  • Python
  • SQL
  • R
  • Java
  • Scala
  • SAS
  • C
  • C#
  • C++
  • Matlab
  • Julia

Given that Python is the most  in-demand language, these are the top Python Libraries for data scientists:

  • TensorFlow
  • Scikit-learn
  • PyTorch
  • Pandas
  • NumPy
  • SciPy
  • Matplotlib

3. Having An Automation Mindset

Automation is bound to be the top technological trend in 2021. Because of COVID-19, businesses have recognized the importance of having AI-powered automation in their business processes in this digital age. According to predictions made by banks and insurance companies, there will be an 86% increase in AI-driven automation by 2025 and Gartner also predicted that hyper-automation will be a key strategic trend in 2021 and beyond.

People in the field of data science should make it a point to make autonomous models a priority while designing an algorithm. Having this mindset will also save you time by automating repetitive tasks, freeing you to focus on more complex projects. Since automation is the future, a data scientist must be ready to embrace the technology.

4. Develop Business Acumen

In this day and age, data scientists must be business scientists. Being driven by data is enough anymore. For anyone to be a part of data science, they need to be driven by business objectives and then use data to solve them. Data is only helpful if you use it to solve the right business problem. Unless you understand the business of the client and the industry they are in, a data scientist will not come close to achieving effective optimization. If the most elegant algorithm doesn't solve the business problem, there's no use to it. And knowing this requires business acumen. In 2021, one cannot get hired as a data scientist without having knowledge about the business and the industry. That's the verdict.

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