Data science is among the one of the most discussed term in last few years. Digital industry is hugely dependent on exchange of data. But most of us just know about basic terminology about data science like it just about studying data and finding out some important information from them. But it is more than that.
So, what exactly those data scientists do? Why does everyone want one? And how do you become one?
Let’s start with what does a data scientist do?
Same as a business analyst, data scientist combines knowledge of computer science and application, modeling statistics, analytics and math to determine insight in data. Evolving beyond the business analyst, the data scientist takes those insights and combines them with strong business plan and effective communication to change the way an enterprise approaches challenge.
The normal day of data scientist involves extracting data from multiple sources, running it through an analytics platform and then creating visualizations of the data.
Now let’s come towards the exact path you should follow to become a data scientist if you are working as developer.
As a developer, you would be curiously looking to re-use your development skills into data science and that a nice thing as a development skill are important part of the data scientist toolbox.
Professionals who are working in IT Industry are generally comfortable with coding, working with data bases and using frameworks. After spending few years in the developing field, you would know at least some languages like Java, asp.net application development, Python and you would have worked with several databases including SQL and Oracle. Here are some set of skills which any good IT professional possesses and it can be highly beneficial for data science domain as well.
- Coding Experience
- Logical thinking
- Numerical Ability
- Database Knowledge
Where should you start?
The main problem in current advanced digital world is everything is available in the plenty of amount. Try searching for the resources on internet for Python or data science and you end up with a long list of resources. So, here is the trick. Talk to a few people who have made the switching and they would add a few more resources, which worked for them.
Getting ready with your machines:
- The hardware,
- The Operating System
- The Software
If we talk about which are the right software you should begin with then go for MS Office, FileZilla, Git and GitHub, Oracle Virtual Box, Terminator if you are using Linux.
- Start with Python: If you completely ready and comfortable with Python you saved yourself a lot of time and you should be ready to move to the next phase. If you are completely new to Python, you would need to get yourself comfortable with any one of them, to begin with. This programming language is great for beginners who are new to programming. A great way to learn the Python language is to start doing it. Start doing the easy one gets comfortable with the syntax and then move onto medium and tough ones.
- Go for SQL too: if you are also new to SQL then you should spend some time learning SQL as most of the data especially the enterprise data resides in relational databases, hence it becomes key to understand how to query them and you can find the best resource out there to practice SQL.
- Apply machine learning: This is probably one of the most interesting parts of the journey as you gain some new skills and concepts here. Make sure to select one online course from the thousands of courses available out there and make sure to complete that. It is a very good thing; the courses have been curated and listed here based on their popularity.
Now its time to get your hands dirty- Your first project
It is high time to get your hands on your first project. Like programming or writing codes, the best way to learn data science is to do data science. Therefore, let us start by taking up a problem to work on. You can select any of our practical problems.
Follow the steps in the learning path
Now that you have successfully done your first project, now check out learning path on R and Python and follow them step by step. Try as much as possible and look at all the machine learning resources available at the online platform. Remember one thing, the best way to do data science is to learn data science thoroughly.
Take part in the competitions
Time to step in battleground and test your learned skills! An advantage of being a part of analytics community is that you get to access so many thrilling ways of learning concepts. You no longer need to follow your traditional ways of learning. Many data science competitions get organized across the globe where you can participate and win prizes too.