Don’t let the noise of others’ opinions drown out your own inner voice.
To be honest, my inner voice always told me to believe I am good at numbers & communication, and no matter how many wrong paths I took, my boat sailed all the way to the shore I was meant to be on.
Before I reveal how I got introduced to this phenomenal field –Data Science & Analytics, I will take you through what other jobs I tried my hands on. Moreover, I wouldn’t say I have reached the pinnacle of success, but I am sure I have secured a right track at least for now.
Well, my journey till here is filled with lot ups and down. Read my full story to know more.
How it all started?
I was in my pre-final year at college, I honestly had no clue what profession I am going to be in after I finish my graduation (like most Indian students). I bagged an internship at a startup as “Trainee Android developer”.
Well to be honest, I did like the idea of creating stuff, but I never had that thing for core software development. The only thing that carried me to take this internship was my passion for creating the beautiful user interface(s). I did go through some MOOCs on android development before taking up the internship. After 2 months of creating apps, I started to get the feeling that it was not my thing. Well, that definitely doesn’t imply that I was not doing well at it, but I just could not see myself doing that for the rest of my life.
Quick Take Away: Finding your passion in life is essential.
Getting the Flavour
It was early in 2015 when I got a flavor of this amazing field. As I recall, it was after one of those popular questions all aspiring data scientist ask “Which programming language for Data Science- R vs Python vs SAS?”
The best answer to this question could be found nowhere else but on this phenomenal website – Analytics Vidhya (AV). That’s how I got introduced to AV. From that day onwards I have been spending a significant time of my life on AV. But the real challenge was yet to show up.
Did I grab a job in analytics yet?
NO! So it was still not a merry time for me.
Quick Take Away: The journey starts with one thing, no matter how long or short.
The Struggle is for real and is always worth it
I started to apply for data science jobs, but most of the positions sought Masters / PhD students (especially in statistics). And besides that, I also started looking for predictive business analytics courses [classroom], as I was out of college by this time.
Learning from MOOCs is not easy and is time-consuming especially when you have a job in a totally different field. I must admit perseverance was the key that got me through it. I kept following AV and started attending most of the Data Hackathons they conducted especially offline ones. It was one great place to meet people of same mindset, which allowed me to learn most tricks for data science.
I also picked up some basic courses on Coursera, Excel to MySQL techniques for business is one. Initially, I use to score miserably in competitions but I just kept on trying. Remember, even if you win the competition or not, you are always taking away good amount of knowledge – so keep on participating in competitions. In my case, I realized I was the kind of person who performs the best if there is a deadline associated with it.
Meanwhile, I was working as an Analyst under Property & Casualty vertical of Insurance. My nature of work didn’t require me to use much of the cutting edge technologies. In spite of that, I always tried looking for ways to use new tools and techniques in order to improve the way I did my job, which of course allowed me to learn a lot.
One thing to my readers -“No matter how difficult the path to your dream seems, just getting back at it again & again and yet again makes it easier than ever”.
I am more than thankful to Analytics Vidhya for laying out a path via ”The Ultimate Plan to Become a Data Scientist in 2016”. By sticking to this path it can land you into wonders, trust me.
Word of caution: Data Science & Analytics is a research oriented field don’t just pursue it because there is a lot of hype behind it. If you don’t like exploring your brains off on a particular case / topic in order to yield few but meaningful information, sorry to say this field is not meant for you.
The Win – Win Situation
Like they say, it’s 10% luck, 20% skill, 15% concentrated willpower, 5% pleasure, 50% pain and a 100% reason to win the game. Well, this shouldn’t be taken literally but yeah a typical success story comprises of these.
The few things that I realized defines your success in this field are:
3.) Discretionary Efforts
4.) Communication Skills (is like bread and butter)
5.) Tools and Techniques (must have).
Having said that, I choose my way to success by being hell-bent to learn all it takes to be in decision engineering (Data Sciences). In the span of 8 months of my job as an Analyst, I practiced on different data science tools at my workplace.
The skills which I learned on my way allowed me to relate the conceptual problems into pragmatic approach of solving the problems. Well, to some extent 10% of my luck also played an important part in landing me a Data Science job.
Fortunately, later that year my organization declared a new business vertical- Data Science. After finding out this I was the happiest person in the organization (I bet there were many more though). Every discretionary effort I made until now determined the probability of my inclusion in this Business vertical. I applied for the position with a portfolio of all my projects by my side. In addition to this, I coordinated the introduction session to Data Science vertical which allowed me to closely meet the Business unit head that later led us into further interaction(s).
My Portfolio of projects, AV-Data Hack Meetup Winner Certificate, Technical Skills and Discretionary Efforts (like I have mentioned before) made me nail this position. Working as a data science analyst requires one to be a quick learner, every new tool brought into the arsenal should be grasped really quickly. It’s quite incumbent to have
It’s quite incumbent to have an insatiable passion for learning new things if you want to be a successful data scientist. I have not achieved success yet, but I am pretty sure I have embarked on the right path. After all, the only thing that determines the ultimate success is nothing but perseverance.
My Advice to People
Thinking like an entrepreneur really helps, a successful data scientist shouldn’t restrict themselves to only building models. One should also find opportunities to get involved in other vital roles of Analysis process.
Tools and techniques are important, but only being an adept at using them won’t lead to ultimate success. Both hypothesis generation and data preparation are equally important. One who is not able to communicate the insights drawn from the analysis process would never fall in the category of Data Science unicorns.
Which is more important for a Data Analyst: Intellectual Curiosity/Intuition V.S Deep Statistical Knowledge?
Intellectual curiosity is the most important thing. The understanding of statistics can be gained, but the curiosity is more innate, if you’re not naturally into working with data, you can be a fantastic analyst, but you become a specific type of analyst. These analysts may not care enough to build a better model.
They lack creativity and the determination to go the extra mile. They’ll need hand holding and will struggle to resolve new problems. But being curious to know more insights about the data you can overcome the lack of statistical knowledge.
People willing to pursue a successful career in this phenomenal field should be inclined towards “Using data science than doing data science”. Essentially, one should not only be good at providing solutions to problems at hand, but also be able to identify data problems and take accountability of “using” data science to structurally solve these problems. An analyst is only as good as their ability to independently work on problems. So, break free from whatever is holding you back.
About the Author
Disclaimer: Our stories are published as narrated by the community members. They do not represent Analytics Vidhya’s view on any product / services / curriculum.