One of the common queries I come across repeatedly across several forums is “Should I become a data scientist (or an analyst)?” The query takes various forms and factors, but here is a common real life anecdote:
When I reflect back on how I took the decision, I realized – I happened to be lucky! The decision was relatively easier for me. Why? I knew the industries / roles, I would not enjoy – these included roles in Sales, roles in Physical engineering and a few others. I was open to roles in data science in retail banks and investment banks and luckily ended up with Capital One.
Today, after spending ~8 years in the industry, it is far easier for me to guide and mentor people on whether Analytics is the right role for them or not. So, I thought, I’ll try and put my thoughts in a framework and share it with the audience of this blog. The aim of this post is to help those people who are sitting on the fence and thinking which job / role is right for them. So, if you are someone deliberating a move in data science or are wondering whether you are a right fit with this industry, here is a neat framework which might help.
I have put a framework in the form of a very simple test. This test is based on the attributes every analyst should possess. You should score yourself against each of the questions (out of the score mentioned after the question) and then add your scores. A good analyst should score more than 70 and any one scoring below 50 should seriously re-consider a decision to be a data-scientist.
By love I don’t mean like, I don’t mean you don’t mind numbers – I mean, do you have an obsession with numbers! Do you love doing guess-estimates at any time of the day – I have done those estimates while I am taking a shower, while I am driving, while I am watching a movie or even when I am swimming (and lost my count of laps)! I know my friend Tavish doesthese calculations in his mind too – while he is driving or while he is playing badminton. If you want me to space out of a discussion, just ask me a really hard logical problem!
5 – dread mathematics & statistics, but can face to some extent
10 – Comfortable with mathematics and statistics, but need calculators and excel to work on problems. Don’t mind attempting puzzles
20 – Can’t live without number crunching and logical puzzles – an obsession!
An analyst will inevitably be tested against unstructured and amorphous business problems. And it is how you solve these unstructured problems, what decides how good or bad analyst you are. My first project in my first role stated: “In last few months, we have seen a high increase in high risk customers of type X. You need to come up with a data based strategy to measure, control and improve this situation.“
Even the business did not have a clear definition of these customers. Can you handle this kind of ambiguity and provide a direction on your own? Do you enjoy these situations or you would rather be comfortable in a more defined role?
5 – Have tried these problems in past – but not my cup of tea!
10 – A score of 10 would mean, you like solving these problems once in a while (say 3 – 6 months)
15+ – You prefer unstructured problems over structured. You don’t enjoy some one else structuring problems for you.
Data Innovation Summit 2017
30% off with code 7wData
Big Data Innovation Summit London
$200 off with code DATA200
Enterprise Data World 2017
$200 off with code 7WDATA
Data Visualisation Summit San Francisco
$200 off with code DATA200
Chief Analytics Officer Europe
15% off with code 7WDCAO17