In 2017 Big Data gave way to AI at center stage of the technology hype cycle. The practice of data science and machine learning capabilities are increasingly being adopted across a wide range of industries and applications. The challenges in data analytics are now being addressed by machine learning. Machine learning, AI, and Predictive Analytics have been the top buzzwords in 2017. We witnessed a growth in value-producing innovations around data this year.
Surprisingly, I got a huge response from many top data scientists from different industries who all shared their thoughts and advice -- which I found very interesting and practical. To learn more about the main differentiators between a good data scientist and a rockstar data scientist, I kept searching on the internet… Until I found this article on KDnuggets. So I distilled all the information and listed down the skills to become a rockstar data scientist. Practically speaking, it's impossible for a data scientist to have all the skills listed below. But these skills are what make a rockstar data scientist different from a good data scientist, in my opinion. By the end of this article, I hope you'll find these skills helpful throughout your career path as a data scientist.
It needs a mix of problem solving, structured thinking, coding and various technical skills among others to be truly successful. If you are from a non-technical and non-mathematical background, there's a good chance a lot of your learning happens through books and video courses. Most of these resources don't teach you what the industry is looking for in a data scientist. In this article I have discussed some of the top mistakes amateur data scientists make ( I have made some of them myself too). And we will also look at steps you should take to avoid those pitfalls in your journey. Many beginners fall into the trap of spending too much time on theory, whether it be math related (linear algebra, statistics, etc.) or machine learning related (algorithms, derivations, etc.).
So, you want to go for the "Sexiest Job of the 21st Century"? You should get started with LinkedIn. It's not only a great place to network and find your next career opportunity. LinkedIn is also a great site for learning and staying updated with the latest tools and industry trends. In order to build a great Data Science LinkedIn feed follow these top Data Science Experts on LinkedIn.
So you've decided data science is the field for you. More and more businesses are becoming data driven, the world is increasingly becoming more connected and looks like every business will need a data science practice. So, the demand for data scientists is huge. Even better, everyone acknowledges the shortfall of talent in the industry. However, becoming a data scientist does not come easy. It needs a mix of problem solving, structured thinking, coding and various technical skills among others to be truly successful. If you are from a non-technical and non-mathematical background, there's a good chance a lot of your learning happens through books and video courses. Most of these resources don't teach you what the industry is looking for in a data scientist.