Data is everywhere. The amount of data generated today across different mediums is manifold compared to what it was a decade ago. The shifting technological trends in parallel have ensured that more and more companies are subscribing to the idea of taking smarter decisions that are data driven. To this end, they look to hire who we call now ‘ the data science professionals’. They serve various roles but ultimately work toward a common goal for the organization. These roles vary in expertise and are many, like, data scientist, data engineer, data analyst, data architect, and more…
Anybody, really. There is a lot of hype surrounding the field and it is obviously due to the heavy demand and the high salary of data scientists. This has led to a lot of people considering a career as a data science professional. But not all are cut out for it. There are skills of the data scientists that one learns and there are some that’s just you. One such skill is the aptitude with data. You can learn to work with the data, but to handle such huge amounts of data does require special affinity towards working with gobs of data that don't make sense. If you believe you can work with it, then rest everything can be learnt and honed to perfection. Once you have learnt the skills required, then`you can get a job as data science professionals depending on your core skills. Here’s how to become a data scientist.
You can take classes in data science online courses or from MOOCS, just browse and collect information from the internet on data science. But a better and more streamlined approach will be to take a data science course offline in your city or online and then you need to supplement this knowledge with books. Information in the books are in order and you can go in order and with minimal distractions. All of these are not possible using the internet. The Internet, without doubt, is the largest source of data available, some of which is information. But there is no proper sourcing and citations and many times, we get unauthenticated information floating around, which if you end up learning is not good for you. Other than this, use the data science roadmap to figure out what skills you need to learn.
Books are written by field experts after painstaking research and there is always an information trail to backup the claims made by a book. The authors stake their reputation on their work. Also, the books have the info you seek and not much more, so there are less chances of diversion from the topic you are studying. Additionally, you will be spending maximum time glued to your computer or device. Books will let you have a change in pace and scene. Books are the best source of structured information even in the age of the internet. So it is advisable that every data scientist has his own bookshelf of resources. Learn More: Differences between Data science and Artificial Intelligence. Artificial Intelligence and Machine learning. In the upcoming section, we’ll see some of the most important literature that every beginner and intermediate data science should have read. These books can be used for self-paced learning of data science.