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This is Part 2 of the skills needed to become a data scientist. Many people talk about hard skills when it comes to being a data scientist. Companies will list different tools and software that they would like you to know about, but when you are at the interview, what matters most is how you perceive yourself.
This comes from your social skills and your personality.
So instead of yammering on, let’s get right into it.
Communication is key. You’ve probably heard that many times and it can be very annoying, but it’s important. Especially when working in a technical field, it is very important to be able to communicate these technical concepts to non-technical stakeholders. Remember that not everyone is technically inclined and you will need to ensure you have effective communication to explain valuable insights, findings from your analysis, and data-driven decisions.
Dealing with complex, unstructured problems every day requires you to be a problem solver. You will need to analyze the task, break it down and discover the problems with the proposed solutions.
You may not be able to instantly look at a piece of data and find the problem right away, which is why problem-solving skills are important.
As part of your problem-solving skills, when you are trying to find solutions to your problem or task at hand, you must be a critical thinker. He must understand the problem he is facing and how he will choose the right methods for its solution.
This includes assessing data quality and how to interpret results to make data-driven decisions and avoid bias.
You will need to have a good understanding of the business model and implement business skills. You’ll always have to keep in mind: ‘How is this company going to use these analytics?’ When you have a complete understanding of this, you will be able to figure out what to do with analytics, such as creating an app, a report, etc.
As a data scientist, you will manage multiple tasks throughout the day. Juggling these tasks can take its toll and make you feel frustrated very easily. Managing your time will relieve you of stress.
Once you’ve done some testing of what the lifecycle of a data science project looks like, you’ll be able to understand how much time each phase requires. You can then use this experience to manage your tasks, such as data cleaning, analysis, and more, more effectively.
Going hand in hand with time management, you will see that having an effective method and process for the data science project lifecycle requires teamwork. As a student data scientist, you will be the only person working on the project. Once you start with a company, these tasks can be divided among the data science team. Not only does it effectively take the workload off your shoulders, but it also allows all team members to experience the included tasks.
Teamwork is only effective when there is communication. Remember this! Always communicate with your team members about what you are doing, if you are stuck on something, or the outcome of your task.
Data science projects consist of cross-functional teams, so you’ll need to collaborate with other experts, such as business analysts, product managers, and more.
As I mentioned before, part of your communication skills is understanding that each stakeholder may or may not be technically inclined. Therefore, you will need to keep this in mind when narrating and presenting your analytical findings.
You can practice your data storytelling skills through blogging as it is a good way to explain technical concepts in a simpler format. Presenting your findings can be done through PowerPoint presentations, data visualizations, and more.
Practicing them will make your life easier as stakeholders will have fewer questions because of the way the findings were presented.
Working with a company and taking care of day-to-day tasks will help you develop your skills and become more competent. However, you will need to go further when working in a field that is highly innovative.
Whatever you are interested in, I highly recommend that you be an expert in that field. This allows your skills and knowledge to be transferable and you can apply them in your daily tasks.
In a field that is constantly evolving, staying on top of things is very, very important. Your learning won’t end once you land your first data science job. You will be constantly learning new things and you will need to take time out of your work day to learn about these things.
I’m not saying you have to go crazy about getting back into education, but you will need to read articles, news, and learn how new tools and software work. This will increase your skill set and make your daily tasks more efficient.
As a data scientist, you will work with sensitive information. There are ethical guidelines you should follow when collecting, using, and sharing data. You must remember that some data is private information, therefore, what you do with it is very important.
You want to analyze the ethics, biases and security around your company’s processes and policies.
I hope this has been a quick and easy guide to the soft skills you need as a data scientist. You will develop and progress many of these skills naturally in a work environment, but it is always good to know what you are up against.
Happy learning!
nisha arya is a data scientist, freelance technical writer, and community manager at KDnuggets. She is particularly interested in providing professional data science advice or tutorials and theory-based data science insights. She also wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. A great student looking to expand her technological knowledge and writing skills, while she helps guide others.
