Learning data science can be drastic. Especially so, when you are just starting your journey. Then many questions rises in our mind that which software or tool we should use to learn- C++ or Python? Which plans to focus on? Do I need to learn to code? These are the questions you need to answer a part of your journey.
That is why I thought that I would create this guide, which could help people starting in data science. The idea was to create a simple, not very long guide that can set our path to learning data science. This will help through our difficult period.
These are some of the tips, which will help to start your successful data science journey!
So let’s get started,
(1). Pick the right role
There are a lot of varied roles in the data science industry. A data analyzer expert, a machine learning expert, a data scientist, a data engineer, etc. are a few of the many roles that you could go into. Depending on your work experience, getting into one role would be easier than another role.
What to do, if you are not sure about the differences or you are not sure what should become? I few things which I would suggest are:
· Talk to people in the industry to figure out what each of the roles entails.
· Take mentorship from people-request them for a small amount of time and ask the best questions. I’m sure no one would refuse to help a person in need!
A point to keep in mind when choosing a role: don’t just directly jump on to the role. You should first understand clearly what the field requires and just prepare for it.
(2). Take a course and complete it
Now that you decide on a role, the next logical thing for you is to put in dedicated effort to understand the role. This means not just going through the requirements of the role. The demand for data scientists is big so thousands of the courses and studies are out there to hold your hand, you can learn whatever you want to.
What can you do is to take up a MOOC which is freely available, or join a program that should take you through all the twists and turns the role entails.
(3). Choose a language/tool and stick to it.
As I mentioned before, it is important for you to get an end-to-end experience of whichever topic you pursue. A difficult question which one faces is getting hands-on is which language/tool should you choose?
This would probably be the most asked question by the beginners. The most straight –forward answer would be to choose any of the mainstream tools/languages there are and start your data science journey.
· You can learn python for data science
(4). Join a peer group
Now that you know which role you want to opt for and are getting prepared for it, the next important thing for you to do would be to join a peer group. Why is this important? This is because a peer group keeps you motivated all time. Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier.
Even if you don’t have this kind of peer group, you can still have a meaningful technical discussion over the internet. There are online forums that give you this kind of environment. I will list a few of them
· Analytics Vidhya.
· Stack exchange.
· Reddit
(5). Focus on practical applications and not just theory
While undergoing courses and training, you should focus on the practical applications of things you are learning. This would help you not only to understand the concept but also give you a deeper sense of how it would be applied in reality.
(6). Follow the right resourses.
Read articles and newspapers about data science every day and make it a habit to be updated with the recent happenings. But there may be many resources for data science.
(7). Work on your communication skills.
Make your friends with good communication skills hear your intro and ask for honest feedback. He will definitely show you the mirror.
(8). Network, but don’t waste too much time on it!
Actually, a meet is very advantageous when it comes down to making your mark in the data science community.
· It gives you inside information of what’s happening in your field of interest.
· Help you to have mentorship support.
· Help you search for a job.
Note-Demand for data science is huge and employers are investing significant time and money in data scientists. So taking the right steps will lead to exponential growth. This guide provides tips that can get you started and help you to avoid some costly mistakes.
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