Want to Become a Data Scientist? Part 2: 10 Soft Skills You Need
A quick 10-step soft skill guide on what you need to become a Data Scientist.
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This is Part 2 of the skills required to become a data scientist. A lot of people speak about hard skills when it comes to being a data scientist. Companies will list out different tools and software that they would like you to know, but when you’re at your interview it is how you perceive yourself that matters the most.
This comes from your soft skills and personality.
So rather than blabbering on, let’s just get right into it.
Communication is key. You’ve probably heard that so many times and it can get very annoying - but it matters. Especially when you’re working in a technical field, it is very important to be able to communicate these technical concepts to non-technical stakeholders. Reminding yourself that not everybody 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 and unstructured problems every day requires you to be able to solve problems. You will need to comb through the task, break it down and figure out the issues with proposed solutions.
You may not be able to instantly look at a piece of data and find the issue straight away, this 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 need to be a critical thinker. You need to understand the problem you’re facing and how you will choose the appropriate methods towards your solution.
This includes evaluating the quality of the data, and how you interpret the results to make data-driven decisions as well as avoid biases.
You will need to have a good understanding of the business model and implement business skills. You will always have to keep at the back of your mind: ‘How is this company going to use this analytics?’. When you have a well-rounded understanding of this, you will be able to figure out what to do with the analytics, such as creating an application, a report, etc.
As a data scientist, you will manage multiple tasks throughout your day. Juggling these tasks can take a toll, and get you frustrated very easily. Managing your time will relieve you from stress.
Once you've had a few trial runs of what a data science project lifecycle looks like, you will 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 in place requires teamwork. As a student data scientist, you will be the sole person working on the project. Once you start with a company, these tasks can be split up between the data science team. Not only does it effectively take workload off your shoulders, but it gives everybody on the team to experience the tasks included.
Teamwork is only effective when communication is in place - remember this! Always communicate with your team members about what you are doing, if you’re blocked on something, or the outcome of your task.
Data science projects consist of cross-functional teams, therefore you will have to collaborate with other experts such as business analysts, product managers, and more.
Storytelling and Presentation
As I mentioned before, a part of your communication skills is to understand that every stakeholder may or may not be technically inclined. Therefore, you will need to take this into consideration when narrating and presenting your analytical findings.
You can practice your data storytelling skills via blogs, 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 these will make your life easier as stakeholders will have fewer questions due to the way the findings were presented.
Working with a company and dealing with day-to-day tasks will help build your skills and make you more proficient. However, you will need to go above and beyond when working in a field that is very innovative.
Whatever it is that you’re interested in, I would highly advise you to be an expert in that field. This allows your skills and knowledge to be transferable and you can apply this in your day-to-day tasks.
In a field that is constantly evolving, keeping on top of things is very very important. Your learning will not end once you land your first data science job. You will be constantly learning new things, and you will need to dedicate time out of your working day to learn about these things.
I’m not saying you have to go full blown crazy back into education, but you will need to read articles, news and learn how new tools and softwares work. This will increase your skill-set and make your daily tasks more efficient.
Governance and Security
As a data scientist, you will be working with sensitive information. There are ethical guidelines that you will need to follow when collecting data, using it, as well as sharing it. You need to remember that some data is private information, therefore what you do with it is very important.
You want to look into the ethics, bias, and security around your company's processes and policies.
Wrapping it up
I hope this was a quick and easy guideline on the soft skills that you need as a data scientist. A lot of these skills you will naturally build and progress in a work setting, but it is always good to know what you’re up against.
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.