My Journey to Data Science
By: Jungmo Kim
Tags: Data Science, Supply Chain Management, Excel, Flatiron School, Python

“What’s wrong with supply chain?” “Why are you taking a risk?” “Why do you want to be a Data Scientist?”

Before I decided to start a journey in Flatiron School, I’ve been asked this question a lot. Just to be clear, there is nothing wrong with supply chain management (SCM). I loved SCM and made a career of it. I still love it. Yet, there were questions that came up, and I couldn’t answer it with given knowledge.

What is Supply Chain Management?

“Supply Chain Management (SCM) is the management of the flow of goods and services and includes all processes that transform raw materials into final products. “ — Investopedia

I have worked in Aerospace industry as a production planner and also worked for consumer electronic company as a demand planner which takes some part of the big supply-chain processes. The main responsibilities of my job were forecasting the demand, planning the demand and supply, and reporting to upper management, and vendor management.

This is where a pivotal question appeared.

When I was working in a field of supply chain, I had questions about what are the data tools (or program language) that can perform better than Excel spread worksheet? How can I create much more accurate forecasting model? And how can I manipulate big data more efficiently?

These questions came across because in daily routine of my job, I had to deal with many different versions of excel worksheet with countless rows and columns of data-set that often crashed my laptop. Thus, I jumped into a research mode and found out there were many tools exists such as R, Python, Tableau & Power BI (visualization wise), and SAS where most of data scientist or analyst would normally use.

I googled “How to become a data scientist.” Best option out there seemed like going to data science bootcamp. However, at the time, I was satisfied with what I was doing; therefore, I simply didn’t wanted to take a risk of going into new career.

Years passed. I had a chance to interview at a company, and hiring manager there asked me “If we are planning to sell Christmas tree for Christmas, how are you going to forecast and plan the demand?”

“What data is available?” I asked and the hiring manager replied “Nothing. Assume this is a new product launching.”

To be able to provide a satisfying answer, I needed sufficient knowledge and experiences from the past, which I lacked at the time. As a result, my answer could not satisfy the manager. After the interview, I was reconsidering about pursuing data science as a new career because I believed if I expand my insights of real world data, I could have better approach to those kind of questions in the future. Thus, I started to google again.

Data Science became a thing.

One of my colleagues sent me information on data science bootcamps. When I read the curriculum and options at the Flatiron School, the phrase “without a data, no story to tell and no insights to share” intrigued me. I started pre-work to get an idea of how data scientist are making impacts to a world. I felt like the tools were in here to help me explore and understand the story of data and how data can transform into something meaningful. I was also surprised with the power of Python.

Python vs. Excel

For example. Below is the data that I made for myself after purchasing 2016 Honda Civic to calculate distance traveled and mpg and scored my drive skills according to mpg efficiency.