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This is the second part of the skills required to become a data scientist. Many people talk about technical skills when it comes to being a data scientist. Companies will list different tools and software they would like you to know about, but when you go into your interview, it’s how you perceive yourself that matters most.
It comes from your soft skills and personality.
So rather than chatter, let’s get straight to the point.
Communication is essential. You’ve probably heard this many times and it can get really annoying, but it’s important. Especially when you work 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, the results of your analysis, and data-driven decisions.
Dealing with complex, unstructured problems on a daily basis requires you to be able to solve problems. You will need to go through the task, break it down and understand the problems with the proposed solutions.
You may not be able to instantly look at a piece of data and find the problem immediately, which is why problem-solving skills are important.
As part of your problem-solving skills, when trying to find solutions to your problem or task at hand, you need to think critically. You need to understand the problem you are facing and how you will choose the appropriate methods for your solution.
This includes assessing data quality and how you interpret results to make data-driven decisions and avoid bias.
You will need to have a good understanding of the business model and implement commercial skills. You’ll always want to keep in mind: “How is this company going to use these analytics?” “. When you have a good understanding of this, you will be able to know what to do with the analytics, like create an app, report, etc.
As a data scientist, you will manage multiple tasks throughout your day. Juggling these tasks can take a toll and make you frustrated very easily. Managing your time will relieve you of stress.
Once you’ve done a few tests of what a data science project lifecycle 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 efficiently.
Going hand in hand with time management, you will see that establishing an effective method and process for the lifecycle of a data science project requires teamwork. As a student data scientist, you will be the only person working on the project. Once you start at a company, these tasks can be divided among the data science team. Not only does this effectively relieve the workload from you, but it allows everyone on the team to experience the included tasks.
Teamwork is only effective when communication is in place – remember this! Always communicate with your team members about what you are doing, if you are stuck on something, or about the outcome of your task.
Data science projects are made up of cross-functional teams, so you will need to collaborate with other experts such as business analysts, product managers, etc.
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 take this into account when storytelling and presenting your analytical results.
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 via PowerPoint presentations, data visualizations and much more.
Putting them into practice will make your life easier because stakeholders will have fewer questions due to the way the results were presented.
Working with a company and handling daily tasks will help you develop your skills and make you more competent. However, you will have to challenge yourself when working in a highly innovative field.
Whatever interests you, I highly recommend being 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 constantly evolving field, staying on the cutting edge is very, very important. Your learning won’t stop once you land your first data science job. You will constantly learn new things and you will need to carve out time in your work day to learn more.
I’m not saying you need to go completely crazy back into education, but you will need to read articles, news, and learn how new tools and software work. This will increase your skills and make your daily tasks more efficient.
As a data scientist, you will work with sensitive information. There are ethical guidelines you will need to follow when collecting data, using it and sharing it. You need to remember that some data is private information and therefore what you do with it is very important.
You want to examine the ethics, bias, and security surrounding your company’s processes and policies.
I hope this was a quick and easy guide to the soft skills you need as a data scientist. You will naturally develop and progress many of these skills in a work environment, but it’s always good to know what you’re up against.
Nisha Arya is a Data Scientist, Independent Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing data science career advice or tutorials and theoretical knowledge around data science. She also wants to explore the different ways in which artificial intelligence is/can benefit the longevity of human life. A passionate learner, looking to expand her technical knowledge and writing skills, while helping to mentor others.