So in my last two posts (here & here), I talked about the six levels to prepare yourself in becoming a data scientist. To recap, the six levels are:

Level 0 - Calculus, Linear Algebra, Statistics
Level 1 - Coding (SQL at least), Data Analysis, Data Visualization
Level 2 - Coding (include R & Python), Data Munging
Level 3 - Machine Learning (Train, Validate, Selection)
Level 4 - Lots of Hands-On
Level 5 - Business Implications of Machine Learning

I did mention in greater detail what you should go for Level 3 and below. You can check out my previous post for it. Let us now touch on the last two levels, Level 4 and Level 5.

Level 4 - Lots of Hands-On

After finishing Level 3, you have the knowledge to start working on projects. Time to get your hands dirty and, at the same time, plug-in knowledge gaps along the way.

Seek out a few datasets that are of interest to you. Put down relevant business questions that you will like to get answered through the data. Be sure to document down your thought process as well, as it can serve as a portfolio for your job hunt later on. As you go along, convert the business questions into data questions, and from data questions into coding/tools.

Do not be discouraged at the start. We are sure to fumble but the more practice we have, the better we will become. At this level, it is equivalent to you having set and passed your theory test and it is now time for the rubber to hit the road and cycle.

Get as much practice as possible!

Level 5 - Business Implications of Machine Learning

At this level, after working on several projects, it is now time to sit down and run through your previous projects. Check out the insights you have given and now convert them into business value i.e. how to use it to make more money, or lower costs for the organization in question.

This is a very important level as this is where your salary is mostly earned. If you are not able to translate what you have done so far into business value, chances are low that you get hired. Read up on the industry or company the data is from, understand their processes, market, etc. Then write down in your project, to be served as part of your project portfolio, how companies after looking at your analysis, can benefit from it. State down clearly and in a business language. Your analysis here will showcase to your future employers your communication skills so do your best!

Conclusion

Congratulations if you have made it to Level 5. You have a project portfolio that can be used as a showcase of your skills and knowledge. You can use it to demonstrate your capability in solving business challenges with data.

I hoped through this movement across the different levels, you discovered what you are passionate about and what you lack thereof. This is a self-discovery process to let you understand if Data Science is really a job for you or something you see yourself in in the future.

I wish you all the best in your learning journey and the job-hunt that follows! :)

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