Thousands of billions of pixels have been deployed to answer the question “What makes a good data scientist?” Most of these articles focused on the skills and tools of data science. Google search for “data science skills” returns 38 million results; “Data scientists traits” give anemic results of 938,000.
Given the range of free, almost free, and paid online training, just about anyone can master the tools and skills that go into the practice of data science. But acquiring these tools and applying them correctly requires a set of traits that are difficult to identify and even more difficult to master.
1. A Love for Solving Problems
The unprocessed data is a bit like a gigantic puzzle. For someone who does not like solving puzzles, it sounds like a big, confusing mess. A good data scientist examines the clutter of raw data and sees an exciting challenge. Find a candidate who can not wait to get your clutter out of your raw data and put it all together to make sense of it.
2. An Inquisitive Mind
A good data seeker is curious to see what secrets the data contains. (S) It does not try to trace the data back to a particular point of view, but rather it seeks to know what truths the data can lead to. Natural curiosity is essential for a good data seeker because innate curiosity drives them when things get tough. Things always happen to be difficult.
Too often we separate people into two categories: left brain people and right brain people. We do not always associate hard science like big data with creative arts, but a good data scientist will have some creativity that will allow him to think about new ways of approaching data, new ways of analyzing data, new questions to ask for the data, and new ways to present the analytical results.
4. Strong Communications Skills
A data scientist often serves as a liaison between technicians and analysis teams and companies. He must be able to hear what business people say and translate that into technical language, as well as understand what the technical side says and translate it into business jargon. Look for a candidate who can easily communicate with both sides of the big data divide.
5. Knowing When to Press On and When to Stop
If you find this candidate who likes to solve puzzles and has a natural curiosity, these forces will push them to overcome obstacles and find solutions when things are difficult. But it is also a trap to become dogmatic and to push when it is time to step back, to let go, and perhaps to scrape or completely rethink a project.
6. Database Design and Management
Whether it’s structured, unstructured, or combined data, the data scientist needs to know how databases work, how to create and manage a database, and how to build a database infrastructure that can handle sets of databases. semi-structured and unstructured data. Few candidates will have a database administrator on their resume, but if you can find one with at least a basic knowledge of database design and administration, this skill is a huge advantage.
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