Despite the easy availability of open source tools & technologies; data science is not a simple & straightforward proposition which can immediately reap benefits. The ability to harmonize large sets of data, train existing systems with relevant data, identify the best-fit models (from the various options) and then applying them to get correct & indicative results takes a good amount of time. More so, more than 3 quarters of the overall data is unstructured in nature, which makes logical analysis even more complex. Hence, the mantra for students is to remain focussed and motivated; and stop not till the goal is reached.

Any one subtopic in data science isnâ€™t that hard, if you have the right background.

What makes DS hard, is the sheer volume of things to learn. You have to figure out what you are willing skip or skim over.

That being said, you do have to have a rudimentary understand of algebra, probability, statistics, linear and matrix algebra, some multivariate calculus, (dot product), algorithms, numerics on a computer, some programming (Python), etc.

If your data is in a DataBase, then you also need to understand SQL.

Also, important, you need to understand the subject domain that you are trying to apply the data science to.