Now that you are somewhat familiar with the basic idea of data-science, let’s dive in!
When I first started learning data-analysis, the problem I faced was that I did not have any guidance on how and where to begin. So without any further ado I’ll cut right to the chase.
Here is the list of steps that you have to follow to become a data-wiz:-
- Languages: Learn R, Python, and SQL
- Tools: Learn how to use data mining and visualization tools
- Textbooks: Read introductory textbooks to understand the fundamentals
- Education: watch webinars, take courses, and consider a certificate or a degree in data science
- Data: Check available data resources and find something there
- Competitions: Participate in data mining competitions
- Interact with other data scientists, via social networks, groups, and meetings
Yes, it is as easy as it looks! 🙂
There are two ways you could follow through Step 1 which , as I like to call it are, the developer’s way and the statistician’s way. The Developer’s Way assumes that you are a developer and are familiar with Python Programming Language, so here you will build upon your already existing python expertise with data-analysis packages like SciPy, NumPy etc. For people unfamiliar with Python there’s the Statistician’s Way, wherein you’ll learn R from a scratch, although prior knowledge of programming syntax would come in handy, but it isn’t necessary, it could be easily learned.
This blog series will first cover R and then move on to the developer’s arena. R is according to me, the best and the easiest language to start with.
As a beginner, signup for the free course in Datacamp and start learning R basics. Don’t bother yourself with any other R courses for now; it will only serve you with confusion. Datacamp in by far the best place to begin, in my personal experience.
Go , get started!
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