I have been asked many times on LinkedIn how to get started on Data Science. Of course, there are some theories you have to go through like what is Machine Learning, what is Summary Statistics, etc. But at the end of the day, these theories that you have picked up will not get you a job. Because at this stage, it is equivalent to a newbie carpenter that has gotten to know most of his tools of the trade.
Think about it, when you buy furniture from a carpenter, what is most important to you? The furniture right and not whether the carpenter knows all the tools available to him or her. So...start working on a few projects.
There are several advantages of working on your own Data Science Project. I will discuss them here.
During the Project
After studying for so long the theory, you want to start applying it so here is your chance! And it is also a chance for your to identify where your skills gaps are and start plugging them in before you apply for a job. The project will tell you where your shortfall is and also where your strength is.
Secondly, you will also start to understand which part of the project excites you and which part doesn't. This is good because it will give you more indication on how to craft your career going forward.
Thirdly, you will have some idea how to go about programming, writing good codes. You will start to understand the importance of having well-formatted codes, how frustrating it is when you have to do debugging. To a certain extent, these will be battle scars that you can be proud of and showcase during any job interview.
After the Project
You now have something to showcase to your potential employers.
Like furniture, each project looks different to different employers. Do not worry. Take in the feedback on your project, based on the questions that the interviewer asked. Use these questions from the interviewer to improve on your project. These questions serve to show how adequate you are as well. Stay positive.
Your project (or portfolio) will also indicate a few things to your potential employers. Firstly, passion! That you are willing to put in non-study/-academic hours on it. Employers who are familiar with data science or artificial intelligence knows that what keeps a person in the field is passion, not money. Thus they are in favor of giving chances to the passionate rather and the project signals that, especially a portfolio.
Secondly is your thought process, on how you have used data to solve a business challenge. At the end of the day, what the employer wants is your ability to solve problems with data. The project that you have done should showcase that. Again, as you go along in your project, you will start to sharpen that thought process.
Remember to accentuate the thought process, the steps that you have undertaken to solve the problem. Document down why you took a certain solution instead of other possible solutions.
Conclusion
After all the theories, start working on one. Stop thinking about whether you have adequate knowledge to work on one. STOP thinking and start taking action! Start taking action, so that you can reap the above benefits that I stated.
All the best!
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