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.
What book would you suggest? There's no fixed path in learning as all roads lead to Rome. Reading materials is definitely a great start to understand the fundamentals which I did the same way as well! Just be aware of not trying to read and memorize nitty-gritty of the maths and algorithms. Because chances are, you'll forget everything without really applying the concepts to real problems when it comes to coding. Just know and understand enough to get yourself started and move on to the next step.
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.).
I still learn new knowledge everyday with my growing passion in Data Science field. To pursue different career track as a graduating physics student there must be'Why' and'How' questions to be answered. Having been asked by a number of people about my transition from academia -- Physics to Data Science, I hope my story could answer the questions on why I decided to become a Data Scientist and how I pursued the goal, and ultimately encourage as well as inspire more people to pursue their passion. The CERN Summer Student Programme offers once-in-a-lifetime opportunity for undergraduate students of physics, computing and engineering to join one of their research projects with top scientists in multicultural teams at CERN in Geneva, Switzerland. In June 2017, I was very fortunate to be accepted to join the programme.
One doesn't need to have an innate talent to become a successful data scientist. Yet, some skills are required to be successful in data science. All those key skills can be acquired by anyone with proper training and practice. In this article, I am going to share some of the important skills, Why they are considered important for a data scientist. Also, How those skills can be acquired. Data Scientists should develop the habit of critical thinking.