Hi, I am Vefa. I have been working for VisionBI as a Data Scientist for over 5 months now. I’m Turkish and live in the Netherlands for almost a year now. I’m learning to speak and write Dutch now, and it’s going quite well actually. Soon I can write in Dutch. You can tempt me at the ‘Big Data Expo’ the 18th and 19th of September at the Jaarbeurs Utrecht. I will be present at stand 78 and would love to tell you more about Data Science and the cool tools I use like Yellowfin Signals.
I would like to briefly explain how I see Data Science and how I work within this field.
Data Science is hot today. Even though the term is new, the job itself has been around for a long time. I define this job as ‘to extract meaningful information from the data and use this to create positive impact in businesses. Whatever that you can think fits into this definition, I see it as my role. Creating fancy reports and models do have a ZERO value if they do not have any impact on the business. A Data Scientist must understand the business needs first. Understanding the problem we will solve is the key. We might need to spend some time to think about it to fully grasp it. As Einstein states ‘If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.’ We must follow this mentality. As mostly technical guys having an arsenal full of powerful tools, Data Scientists tend to apply the fancy methods to data before evaluating its impact and worth. Maybe the solution lays within a simple algebraic calculation rather than applying the fanciest Deep Neural Networks. Plus, it is always better to keep it simple, right? Just choose the simplest and easiest solution that gives you the satisfactory answers required. So, only after this initial step, as Data Scientists, we should start using our arsenal.
I see Data Science as an end-to-end process starting with extracting data and putting the results into action. In order to complete the whole process, a Data Scientist must have varying skills and tools. And not only technical skills but also soft skills like presenting, visualization, communication. It is also really important to express your findings or your methods to non-technical people. At the end of the day, non-technical people are Data Scientist’ customers and customer is the king.
When it comes to technical skills, a Data Scientist must have a basic acquaintance with all major tools. Of course, there will be favorite tools, libraries or languages but knowing your way around also in less favorite tools will help immensely. Preferring Python is cool, but R has more advanced statistical libraries, so a Data Scientist must be able to use both. Personally, I prefer to be the jack of all trades, master of few (not none, for sure).
And last but not least, a Data Scientist must continuously learn and adapt new tools since the tools in Data Science community are increasing with an exponential speed.
So far, I tried to explain how I see Data Science and the macro perspective of working in this field. I will also write a core data science project steps in another post.
I am currently working on several projects and in the coming posts I would like to mention those projects as well. But before that, I want to direct you to a cool product of Yellowfin: Signals. Signals is an automated analysis and discovery tool that helps companies to reduce the manual labor while analyzing their data. Please have a look the introduction to Yellowfin Signals posts that I wrote.
More Signals use cases will also follow soon.
