Introduction
One of the common queries that I come across repeatedly in various forums is “Should I become a data scientist (the analyst)?” The query takes various forms and factors, but here's a common anecdote from real life:
“I have been doing sales for various BFSI giants for the last 3 years, but I stopped enjoying my role. After reading about Business Analytics and Machine Learning, my interest in this area has grown. Should I make a change and learn data science? If so, how I do this?
When I reflected on how I made the decision, I realized: I was lucky! The decision was relatively easier for me. Why? Knew the industries / roles, I would not enjoy; these included roles in Sales, roles in Physical Engineering and some others. He was open to data science positions at retail banks and investment banks and, Fortunately, finished in Capital One.
Today, after passing ~ 8 years in the industry, it's much easier for me to guide and guide people on whether Analytics is the right role for them or not. Then, thought, I will try to put my thoughts in a frame 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 what work / role is right for them. Then, if you are someone deliberating on a movement in data science or wondering if you are a great fit for this industry, here is a neat framework that might help.
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Structure
I have put a framework in very simple test form. This test is based on the attributes that every analyst must possess. It must be scored in each of the questions (out of the score mentioned after the question) and then add your scores. A good analyst should score higher than 70 and anyone who scores less than 50 you should seriously reconsider the decision to be a data scientist.
Test questions:
- Do you love calculating numbers and solving logic problems, In other words, riddles, odds and statistics? (score on 20)
By love I don't mean I like it, I don't mean you don't care about the numbers, I mean, Do you have an obsession with numbers? Do you love making guesswork estimates at any time of the day? I made those estimates while taking a shower, while i drive, while watching a movie or even when I'm swimming (and I lost count of laps)! I know that my friend Tavish also does these calculations mentally, while driving or playing badminton. If you want me to step aside from an argument, Ask me a really difficult logical obstacle!
Wrench:
5 – they fear math and statistics, but they can face up to a point
10 – Are comfortable with math and statistics, but he needs calculators and excels to work on the problems. Don't mind trying riddles
15 – I love doing numbers and solving logic puzzles anywhere
20 – Can't live without number processing and logic puzzles: An obsession!
- Do you enjoy working / handling unstructured problems? (score on 20)
An analyst will inevitably be tested against amorphous and unstructured business problems. And it's how you solve these unstructured problems, what decides how good or bad you are as an analyst. My first project in my first role said: “In the last few months, we have seen a large increase in high risk clients of type X. A data-driven strategy needs to be devised to measure, control and improve this situation.“
Even the company did not have a clear definition of these clients. Can you handle this kind of ambiguity and provide an address on your own? Do you enjoy these situations or do you prefer to feel comfortable in a more defined role?
Wrench:
5 – I have tried these problems in the past, But it's not my cup of tea!
10: a score of 10 it would mean that you like to fix these problems from time to time (as an example, of 3 a 6 months)
15+ – You prefer unstructured over-structured problems. You don't enjoy having someone else structure your problems for you.
- Do you enjoy deep research and can spend hours slicing and slicing data? (score on 20)
Going back to the first project I faced, I take 3 months understand the business, have multiple discussions with stakeholders, put them together on the same page and then extract the data for solutions. You need a researcher perspective to be a good business analyst. When was the last time you spent hours and hours immersed in the response of an obstacle? Can you do that over and over?
Wrench:
5 – You wish for a change every few hours. You can't work on a single problem all day.
10 – Can work on a research obstacle, but you need some extra work to help you get out of boredom.
15 – You feel like parallel work is distracting you from progressing on the key problem you are working on. I'd be happy if they took them away
20 – I can't stand distractions
- Do you enjoy building and presenting stories supported by evidence? (score on 20)
A data scientist must be a fluent presenter. What good is all the hard work if you can't influence your stakeholders? Communicating with data and presenting data-backed stories is one of the most important items in the life of a data scientist. Imagine being part of companies like Google and Amazon: has all the data you need (probably more than that) for the domain you are working on, but you need to turn it into a meaningful story, present it to and influence stakeholders. to make the right decision!
Wrench:
5 – You struggle to communicate my mathematical thoughts to the audience.
10 – You can manage storytelling with lots of practice. I can't think of doing this on the go!!
15+ – Anytime and anywhere!
- Do you always find yourself questioning people's assumptions and are always curious to know “why”? (score on 10)
This is probably the best part and the most fun!! Here is a quote that is read somewhere on Linkedin: Arguing with an engineer is much like fighting in the mud with a pig: after a few hours, you realize that the pig likes.. Equivalently, asking why it comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a justification if they are unclear: Why did you ask this question? What was your thought procedure? Why do you assume? are just some examples of these questions.
Wrench:
5 – Only asks questions when they are critical to asking
8+ – You can't stand the anxiety of not understanding something! Jumping in to ask questions!
- Do you enjoy solving problems and thrive on intellectual challenges? (score on 10)
Analysts need a gift for problem solving. Most of the problems companies would face would be unique to them and it would take a smart solver to solve them. Solutions that work for one organization may not work for another; it should be someone who quickly develops a deep understanding of an obstacle and then discovers innovative ways to solve these problems.
Wrench:
3 – You don't mind thinking about solving problems, but you fight.
6 – You can solve problems sometimes
9/10 – You love the intellectual thinking procedure
Final notes:
What is my score? I would rate between 80 Y 85 in this test. It's your turn now. Take the exam and let me know, How much do you get? At the same time, let me know if you think the test was helpful or not.
Note that, like all subjective questions, there are no right or wrong answers here. You may get a low score on the test, but still, be the best analyst / data scientist that exists. Despite this, the test should help most people facing confusion. If you are still confused after reading this post, feel free to share your confusion / inquire through the comments below. It will help you explain the confusion and help me improve this framework.
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Now that you know you may or may not become a data scientist, you may wonder “How can I become a data scientist?”. Here is the roadmap:
