Image from KDnuggetsFrom a Mathematician to a Data ScientistOpeyemi AborisadeBlockedUnblockFollowFollowingMar 5I grew up with so much interest in numbers.

At a little age, when I see objects, I try to describe them with numbers.

For example, you hear me say stuffs like “I saw 2 black cars zoomed off this afternoon”.

The numbers, shapes and patterns made sense to me.

At the end of each school term I would come home with good grades in Mathematics and anything quantitative but, an average grade in English.

Not that English was difficult, but I didn’t just see the beauty and reasons to study English when that’s my official language.

People usually associate mathematical skills with physics skills.

Well, I was excellent at both.

In fact, I can still recite most of the Physics definitions as a song (kudos to Mr.

Tijani, my awesome Physics teacher).

It was so easy to pick up courses with numbers and formulas.

It was easy to decide what to study at college, Engineering.

But for my parents it was Medicine.

I did get an admission to study Medicine and Surgery at Ladoke Akintola University in Nigeria, but I knew that wasn’t my path.

I missed my matriculated day to be a freshman just because Medicine and Surgery isn’t meant for me.

Holidays was over and most of my friends were all packed and set to resume school as a freshman in college, but my story was different.

My mum being who she is (nice and want the best for me), she enrolled me in a pre-degree program to help get me prepped for college when it’s time.

At this point, my parents were cool with my choice of study.

My cut off mark wasn’t so good for engineering and I was left with choices that my cut off point fit into.

Mathematics was one of the options, and I must say it was easy to finalize that decision.

Fast forward to college day, Mathematics was fun, but the lecturers didn’t make it fun.

Most times I ask myself why I didn’t choose Statistics over Mathematics.

The strive for success was real.

Mathematics isn’t just about the addition and subtraction I fell in love it.

It was much more advanced, but the interest made it meaningful.

I graduated with a bachelor’s degrees in mathematics.

At this point, the thought of having a degree in Mathematics comes with two decisions in my home country.

Either you go into lecturing or you working as an accountant or in a financial industry.

Data Science was not popularly till 2008/2009, even though it has been in existence since 2001.

I didn’t get to know what data science was all about until 2014.

That was about when I started a master’s program at the African Institute for Mathematical Sciences (AIMS).

I heard the words Machine Learning for the very first time from a friend named Mawulolo Ameko in 2015.

We had to come up with a project proposal for a career fair presentation day.

He explained in layman’s terms what Machine Learning was all about and shared with me some links and materials to read up to get better understanding of the concept.

I enjoyed every bit of the research I did to understand what Machine Learning was all about.

Then I wished I knew about it earlier before I started my thesis in Stochastic Control with Application in Finance.

After AIMS, I knew I already found my path.

We must be careful though to avoid assuming critic that Data Science is only Machine Learning, when it’s not.

I knew have got some analytical skills from my two degrees in Mathematics to sustain the initial phase.

I wanted more and I sourced out for another master’s degree, now in Applied Mathematics.

This second master’s degree provided me with courses that tested my analytic skills.

I took courses that range from Mathematics, Statistics and Computer Science.

Which are the three core components of Data Science.

I have found fulfillment learning and growing in Data Science so far.

I have received several messages from folks in Data Science field and those that are in the transition phase into Data Science via LinkedIn and personal messages.

But the truth lies in the fact, becoming a Data Scientist comes with individual passion.

You shouldn’t jump into Data Science because everyone talks about it.

Find your path and ask yourself if you truly fit in it.

Be strong in Mathematics, Statistics and Computer Science.

Then add a bit of programming in language of your choice, be it Python or R.

Explore extensively on Machine Learning concepts.

Build models using Machine Learning algorithms.

Solve the future problems for that dream company of yours, problems like how they will better satisfy their customer and the likes.

Be extraordinary.

Believe in yourself.

Keep up with the trends in the field.

And never stop aiming for the top.

Cheers.

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