Hindsight: 8 months down the analytics road

Well this is probably not my typical piece of how-tos or tutorial on analytic tool or that sort of thing.

This time it's just some random piece of thoughts that I thought I'd share with all, having walked the path for nearly a year now since the beginning of 2015.

I started this journey following a change in the direction of our company. We've decided sometime late last year that big data and analytic had a huge potential in the future of the telecommunication industry, and thus - we should try and revamp our company and adopt this data-driven culture into our daily routine.

Personally I thought it was fun. With all the hype of big data and it's possibilities, not to mention data scientist being a sexy job, I was ecstatic (to say the least) to be part of the bandwagon.

Ahem.

Not to mention that we also had Hadoop in our IT ecosystem. Seemed like the best place to learn and practice big data analytics - I said to myself.

8 months down the road though, I feel nowhere as close to being a proper data scientist.

Not that that meant I wasn't going anywhere or the job itself isn't fulfilling.

The last couple of months was a time of self-reinventing. Of self-discovering. Of experimenting.

And to be honest I actually do enjoy every single day of it (or maybe I just love learning new stuff everyday).

So I don't get to be a data scientists; yet I enjoy my work? Seems oxymoronic doesn't it?

Well let me share with you my experiences during my 8 month journey:

1. Stop focusing on being sexy. You're not paid to be sexy - you're paid to do some menial tasks like massaging data or getting that ETL to work. So get it done. 
2. Repeat this after me - "I am not a unicorn, and I never will be.". If you haven't been working in this field for the last 10 years - chances are you probably can't be an expert on all these things within 2 weeks. So stop putting that pressure on yourself (and say the same to you boss or you HR manager). 

   

   A unicorn

3. Develop yourself into what you believe you should be. Have a plan. Stick to it. You think learning statistics is hard? - I think this is harder. Planning what you should be learning and trying to stick to it involves a lot discipline and determination to see it through. Which what you need when you're trying to absorb multiple discipline of knowledge within limited amount of time.
4. Be focused. While it's nice to know that there are lot of tutorials out there that can teach you machine learning in 5 steps, or such and such URL can teach you R within minutes - the most important thing is have you done anything with them?. If yes - then good for you. If not - then stop crawling the web to act be that enthusiast and start being a practitioner. Coursera? EDX? DataCamp? Ok there Tiger - I know you're eager and excited and all that..take it easy and do it step by step. There no need to take all those courses at once you know.
5. Data science is not all about machine learning. While a data scientist is expected to be well versed with the different methods of machine learning and prediction, the field itself isn't restricted only to those who are experts in forecasting. You could apply your trade in visualization, extraction, massaging, or IT infrastructure management. Trust me - your data scientist can't work without any of those being available (lest they'd have to those things themselves).
6. Be patient. Give yourself sometime to learn and master all the things you need to know before moving on to the next topic. It's pointless to keep jumping back and forth in-between topics and getting confused at the end of your journey. Heck I'm in my 8th month and I'm just starting to do some basic machine learning. Cut yourself some slack and take it easy (unless you're some child prodigy and can chew neural networks for breakfast).
7. Never stop learning. Once you can grasp the topics that you studied, maybe take some time out and practice what you've learned. Experiment with it. Do a Kaggle for kicks (put it in your resume too! ). Try to find some way to apply what you've learned on the field. Business? Process? Healthcare? Or maybe learn about some new field of study like economics, graph theory, or operations research? Learning about new things can give you new perspectives and understanding about problems never before seen in your typical environment, which can be useful in your career.

At some point through my journey (probably during the 4th month or so), I already had my data science-fever worn off. Guess the hype didn't bother much to me anymore. You do kinda get tired of seeing all these articles about how data science and big data is still this next big thing that going on in the industry (and never seem to stop eh?). While the hype has worn off for me, the high level of interest is still there. Being an an actual practitioner (I'd say I'm probably just an analyst for now) after some time really does give you that level of insight into what a data scientist should actually be, and makes you realize that true experts really can't be trained in a 5-day course - they have to be battle-tested with litres of sweats coming from doing research and going through various types of data.