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4 Essential Skills Often Underestimated by Data Scientists

There are more to data science than data

Photo by Pixabay from Pexels
Photo by Pixabay from Pexels

Whenever you look up a roadmap to becoming a data scientist, you are often faced with a list of technical skills that you need to develop and grow in order to start your career. However, most of these guides often miss – or neglect on purpose – are equally as important as all the technical skills but often go underestimated.

A data scientist or a developer is more than just a programmer or a data miner; they are a mix of different roles all in one. They are a little bit of business planers, science communicators, analysts, and idea generators.

Being a Data Science goes beyond writing models with high accuracy; it goes 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.

In this article, I will go through some skills that I believe are essential for a data scientist to has in order to excel in their career. Most of these skills are soft skills with one technical skill that seems to be overlooked most of the time.

Clear and effective communication skills

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

There is a reason for that, in most technical fields, when we hold meetings with other developers and programmers, or even researchers, we need to look well0versed and smart. So, we use fancy words and complicated examples.

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

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 have effective communication goes beyond simply explaining complex ideas. To be an effective communicator, you need to practice expressing your thoughts using the correct words.

Moreover, an essential part of effective communication is using the correct visual aid that doesn’t contradict your words but instead supports it and helps delivers your ideas better.

To become a better communicator, practice doing the following:

  1. Get comfortable with both verbal and non-verbal communications. Body language says as much as the words you’re actually speaking.
  2. Keep things short and to the point. An approach to do that is using the PIP model (purpose, importance, preview). State the reason for your work, why it matters, and then how it works.
  3. Don’t solely depend on your visual aids – graphs and charts. Explain what each of them represents simply as you’re showing them.
  4. Get better with timing. One of the bad human habits is we tend to ramble when we are speaking in public. Having a clear timing goal for yourself will help you avoid that.

Data Visualization 101: 7 Steps for Effective Visualizations

Basic business understanding

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 is 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 specific target. Understanding the basics of business models can help you better understand your data and hence better analyze it.

"Data science is more than just number crunching: it is the application of various skills to solve particular problems in an industry" – N. R. Srinivasa Raghavan

Being a data scientist is not just about numbers; although numbers play a great role in the field, every data scientist needs to know the business model of the field they work in. They need to know why this model is followed and how it is beneficial for the business.

A data scientist that collects and analyzes the data without knowing the business model behind it is like reading a story written in another language but using your native language’s alphabet. You can read it but won’t be able to understand it until you translate it.

You don’t need to go into extreme depth or go to business school to better understand your data. You merely need to know the basics of the specific model followed in your workplace.

Collaboration and teamwork

As a data scientist, you never work alone. You’re always a part of a team working towards the same goal. You will probably need to interact with managers, designers, markets, and, most importantly, clients. And of course, other data scientists.

To be able to grow and prove yourself in such a work environment, you need to be a good team player, open to listening to new ideas and always welcoming constructive feedback. You should remain open-minded and objective while maintaining your unique perspective.

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/ society, and getting to meet others who share the same interests and mindset as you.

Finally, a great way to be a team player is mentoring. When you offer to be a mentor to newcomers, whether, in the field in general or your particular workplace, you won’t just prove that you’re knowledgeable and show that you’re flexible and willing to share your knowledge with others.

The ability to maintain the code

The final skill I want to talk about is a technical skill that we – myself included – seem to overcome when we get into the field. This skill is fluency in version control.

Writing good code with state-of-the-art models is not the end. In software development, there is no end; there’s always something to be added and improved on. Writing a maintainable code and painting that 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 not fun, and in most times, very confusing, but nevertheless, it is an essential skill that every data scientist should master.

If your code is well-maintained, it will be easy to understand by other data scientists. It will be easy to build upon 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.

Version Control 101: Definition and benefits

Conclusion

Being a data scientist is not all about the data. Sometimes it is a mix of business, marketing, and communication. In order to be an influential data scientist, there are some skills that you need to have in your skills-belt.

Skills such as having effective and clear communication skills, some basic understanding of business models, and your ability to be a great team player. These soft skills can transform your career and open up new opportunities and perspectives for your future.

Having a career in data science may seem daunting at first, but I would argue it is so worth it. When you tell a story with the data, find the pattern within the chosen, and help make this world a better place, you will get this amazing feeling of fulfillment that makes you forget about all the hassle you had to go through to get to where you are.


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