This is the age of data! As soon as you open your Facebook account, you are inundated with huge amount of data. You get to see posts from your friends, which could be in the format of text, pictures and videos. Now, just imagine if you could tap into this data and use it gain insights, that would be just wonderful, wouldn't it? And this is exactly where data science comes in.
Data science, analytics, and machine learning are growing at an astronomical rate and companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently. IBM predicts that by 2020, the number of jobs for all U.S. data professionals will increase by 364,000 openings to 2,720,000. We caught up with Eric Taylor, Senior Data Scientist at CircleUp in a Simplilearn Fireside Chat to find out what makes data science such an exciting field and what skills will help professionals gain a strong foothold in this fast-growing domain. Watch the complete Fireside Chat recording here or read on to find out everything new and exciting about data science. People have tried to define data science for over a decade now, and the best way to answer the question is probably via a Venn diagram.
Data Science is an inter-disciplinary field, which deals with algorithms, processes, systems and is used to extract insights from huge amounts of data and improve understanding. Data mined can be in any form – structured or unstructured. Data Science utilizes numerous theories & techniques that are part of other fields such as Mathematics, Statistics, Computer Science, Information Science, and Chemometrics. The emergence of Data Science has been primarily due to the burgeoning growth of data across companies, internet, raising computer power etc. For instance, today an estimated 2.5 quintillion bytes of data is created daily.
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.
Data scientists are highly educated – 88% have at least a Master's degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor's degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data. After your degree programme, you are not done yet.