The data science industry has probably had the turn of the century with the kind advancements it has experienced. This comes as a polarizing shift when the industry was born afresh. And by that one means that with new technology, there grew the demand for newer kind of job profiles, unlike the same, old engineering positions which the field of IT used to labour people with. Gone are the days & one can finally put them behind. But the speed with which this flux was caused made the employers realize that they might have the vision of the technology as well as the means, to produce results but not the people who are eventually required to put their brains to work. To their surprise, even the potential applicants, who have made up scenarios in their minds, to be ready to go through the training have pre-assumed, how tough could the training be?
It might come as an even bigger a surprise, that the road to enter the data science industry is very straight forward.
When we go through the educational backgrounds of the people who have made to let’s say, become a big data analyst, it would be seen that people venture in from a varied set of defined fields. Machine learning, Statistics and mathematics formulate the backbone of this profile. We take it for granted that as aspirants one would at least have a technical degree at the graduate or even Post graduate level. So online training could be commenced to further hone such skills rather than building them from scratch.
Do keep in mind that accredited institutions with classroom training are a bit hard to find. This has been compensated with the availability of online classroom training. Also, students would have access to the learning material 24/7. How about that?
To start with courses such as MIT’s linear algebra could be opted for. for a second option, one could even look for sources such as Coursera where you could find courses like Practical Machine Learning. A next in line alternative could also be an introduction to machine learning by Data camp. People would be advised to learn end-to-end development, moreover strengthening one’s grasp on technical languages like R & SaaS has become a pre-requisite. Those working at the helm of the data science industry do state the importance of understanding databases. The databases which are intensively brought to use include the likes of MongoDB, Cassandra, ORACLE and MySQL.
Speaking of technicalities, one ought to understand the workflow of data science jobs, as a whole. This kind of a process includes collecting data, exploring it, munging, modelling it & validating following which the scientists can report it to their superiors or business leaders. If data science is one hemisphere of today’s’ tech-oriented globe, then big data is the other. Recruiters demand and in most cases expect a good knowledge of Hadoop, Spark & MapReduce.
In the end what matters is that how the findings of such insights are passed on further to make business decisions.