Why did I decide to move into Data Science?

Tom Ribaroff
4 min readApr 2, 2020

I see Data Science being used and mentioned all around me constantly. Forbes magazine published 2 articles with Data Science in the headline in the past 4 days alone (at the time of writing). There are so many reasons why we as a cohort are excited to be studying this! Alongside the reasons that anyone would want to work in this industry (flexibility, good hours and working conditions, the chance to be a part of a dynamic and innovative industry…), I felt that these were the most obvious in my case.

1- An Appreciation for Code

Studying Mathematics at university was always a no-brainer for me. I found it much more satisfying than other subjects and enjoyed the debates that I had in the more philosophical aspects of the subject. When thinking about a career, I knew I wanted to stay inside this area— I wanted something that was going to challenge me logically. I didn’t want to stay in academia specifically, and during my degree, I had enjoyed my modules in statistics, probability, and optimisation. Decision Theory was my best and most enjoyed final year module, on the Mathematics of how we formalise the Mathematical arguments to optimize utility. On top of this, I did modules in Python and Java, where I found the logic to be extremely satisfying. Finding a clean algorithm gives me the same satisfaction as seeing a rigorous and beautiful proof.

Compare a common, lovely proof of infinite proofs with the code we used for finding new prime numbers:

A little loop that we can run to help find prime numbers between 2 and 100

Our loop for creating prime numbers is watertight and doesnt miss out any possible cases

A casual proof as why there are an unlimited number of prime numbers

Our proof of infinite primes is satisfying because the logic is watertight, not missing out any possible cases.

I encourage anyone interested and comfortable with Maths to look up AKS Test for finding large primes, or have a flick through this similar topic:


2- Far-Reaching Scope of Data Science

Data science can be incorporated into most industries to help them operate more efficiently. Data Science has all sorts of Financial applications — and I’m excited to explore those, as most people in the subject are. Given that I am a young and still learning what my goals are, it gives me a lot of confidence in this subject when I hear of Data Science being used in industries that you wouldn’t expect it. Our very own instructor Dave comes from Music Psychology! You’d expect that a Data Scientist being hired by a publishing company would be to find a way to maximise profits, but I’ve heard whisperings from a friend in Publishing that their company has a team of Data Scientists working alongside the Publishers to try to find Algorithms for what makes good writing, and using their findings to help encourage their writers to improve (some coaching I’m sure I could use):


Could we find ourselves one-day using Data Science to help coach the future generations of singers, painters, sculptors? I can’t answer that yet, but I am so glad to hear that my use as a Data Scientist won’t just be to maximise profits, as important as that may be.

3- Frustration when seeing Poor Data Analysis (and therefore Poor Maths…)

It’s a big disappointment that so many of the opinions of the general population are formed as a result of someone else’s poor analysis. Although it can be argued that they know what they are doing, the number of newspapers that publish erroneous headlines which rely on ugly lies has a huge impact on people’s daily lives. There is also a huge amount of corrupt Data Science done in the healthcare industry, where selective data sets are used and ambiguous hypothesis supported to sell products more.


This is such a shame, particularly because we know that Data can be used to influence our society for the better. In my last year of university, as part of my Bayesian Statistics module, I was visited by members of a Data Scientist team at NICE, who were tasked with taking the healthcare budget and maximising the “health” of the population of the UK. Their models helped them analyse what social projects would return the most happiness for pound spent, what medical trials would return the most improvement to people’s “health” and what treatments were worth subsiding as a government. We discussed the deep Mathematical concepts (mainly optimisation models)they used to help them achieve their aims. Clearly, this is a complex, emotive and controversial job and I thought it was a brilliant example of why Data Science is so important, and how in some cases it might be used for a positive impact on people.