Questioning Reality: The Data Science Journey

Is there a God? How was intelligence created? Is the Earth round or flat? If a tree falls in the woods and no one hears it, does it still make a sound?

Questioning the fabric of reality is the first step to the scientific method. And in my case, the first step in becoming a data scientist.

My journey in data science began shortly after I graduated from Rutgers in the summer of 2021. I was a confused, young man with an obscure Bachelor’s degree in Philosophy and 0.00001 Bitcoin to his name. After a week of working for my family’s business, I quickly learned that this was not the path for me. I decided to pivot and acquire a new skill in my free time. I was always interested in mathematics and logic, so I began learning the coding language, Python.

For months, I taught myself to code by utilizing many online resources like YouTube, Codeacademy, and Kaggle. I studied every aspect of the language ranging from printing “Hello World” to the four pillars of object-oriented programming. Once I reached a solid, intermediate level of expertise, I asked myself, “What can I accomplish with this skill?”

During a winter afternoon spent browsing the internet, I stumbled upon Data Science. I was intrigued and perplexed.

People utilizing data to draw conclusions! I didn’t think it was possible. I thought everyone, like me, would take a guess at how things worked.

I was wrong once again.

I further questioned the field, where I learned about the different steps involved such as data cleaning, data exploration, predictive modeling, and data visualization. I was enticed by the process.

However, once my dopamine levels dropped and my confusion once again conquered my mentality, I felt overwhelmed. How do I become a data scientist? Who teaches this profession?

After weeks of going asleep on this question, I finally did what I had never done before. I Googled: “How to become a data scientist?”

I scrolled and scrolled for minutes, and I discovered data science boot camps. There were a plethora of camps, all with similar descriptions. They pitched a “15-week intensive curriculum that prepares YOU with the skills to be a data scientist.”

The next day, I applied to multiple boot camps. In that week, I had many interviews. In the end, I chose Flatiron’s 15-week intensive Data Science Cohort because I liked their curriculum, and more importantly, they offered an in-person section.

On 2/22/2022, I moved to Manhattan. I jumped on the urine-filled subway for the first time in my life and traveled to 11 Broadway next to Bowling Green.

I was nervous. I was not sure if I had what it took to be a data scientist. But I repressed my fears and entered the building.

For the first couple of hours, there was not much discussion about data science. It was mostly orientation, introductions to the other students, and assimilating to the place that I will be at for the next 15-weeks.

After lunch, my instructor Praveen gave his first lecture where he showed this infographic:

He said this is the process that Data Scientists follow to complete their projects and that this is what we will learn in our 15 weeks at Flatiron School.

It was very exciting to read the slide since I knew the vernacular of all those topics — data mining, data cleaning, data exploration, feature engineering, predictive modeling, and data visualization. They all caught my eye immediately.

Although, I was puzzled. Why did data mining have a number 2 in the upper right corner? It should have number 1 because it’s the first step.

I shifted my eyes slightly to the left to see a step that I never even heard or read before this class.

I examined the description, then thought to myself: What relevant questions can I ask to then use data science to answer?

Moments passed.

I had no idea what I could ask. I had no questions. I was previously so obsessed with how to answer questions that I couldn’t fathom how to ask a question.

So, as always, I waited patiently and allowed the answer to come to me.

The hours went by and it was close to the end of my first day at FlatIron. And the answer has not arrived yet.

My instructor Praveen called me over to have a one-on-one session. I approached him with the question dilemma still ruminating in my mind.

He asked about my life and I asked him about his. We talked about our hobbies, our mutual interests, he asked me why I wanted to be a data scientist and I told him everything I wrote earlier in this post. Then he asked me the question: ‘Regarding that infographic I showed on screen earlier, is there any part that you might have trouble on.’

I thought about it for a moment and then I answered him honestly.

“I just don’t know how to ask relevant questions.”

Praveen seemed to be taken back. He said I was the first student to say that. I was a bit embarrassed and surprised.

However, he treated the situation normally. He referenced one of my interests, which was dog breeding. Praveen mentioned how I could analyze data on the personalities of different breeds, and see if there is a relationship between personality types and dog breeds.

Then it clicked.

I could develop questions based on my interests then use data science to find the answer.

Although, some of my interests can lead to unanswerable questions, such as philosophy, and the old-age question on the existence of God. I’m not sure what data if any could be used to answer this question. Also, some questions are already answered like the geometric status of the Earth, whether it’s round or flat. Of course, it’s flat.

Just kidding.

Some answerable questions could be the dog example. Or, I like surfing but I dislike cold water, so I could use data on the moon and the ocean, to see if there is a positive correlation between the gravitational pull of the moon and the temperature of the water.

The list is endless and so is the information in our world.

So yeah, welcome to my data science journey, where I will be questioning reality and using data to answer those questions.