The Data Scientist in Me
“There is no money in mathematics or science. Go choose a profession that will make you lots of money” said my parents.
As a good Asian daughter, I became a material engineer so I could be more practical and make money. Looking back, I wish I would have majored in physics or mathematics, subjects I really loved. Although I enjoyed working as a failure analysis (FA) engineer, I wanted to explore other career options after I resigned and moved to Europe with my husband.
The first career option I explored was health care due to its job security. After one and half years of memorizing anatomy, physiology, and other medical terms, I realized I don’t enjoy pure memorization without clear logical reasoning. I found out reasoning and logic are important to me in a career. It was time to get back to engineering and science.
Programming is crucial for most scientific and engineering professions, so I decided to study Python using massive open online courses (MOOCs). Whenever I was studying these courses, it was like experiencing the effects of Einstein’s theory of special relativity, time just flew by. The courses exposed me to optimization, the central limit theorem, and machine learning. As soon as I finished the curriculum, I looked at data science programs. I chose data science professional certificate in R because I wanted to learn another programming language. Although I didn’t fall in love with R as I did with Python, time still flew by. Towards the end of the program, I discovered I have always had a data scientist in me even when I was working as an FA engineer. Both professions require the process of understanding various domains to figure out the right questions, performing data analysis, creating visualizations, compiling reports, and presenting findings to make important business decisions. Data science is just FA engineering without a billion dollar Fab with its digital data stored in super computers or floating around on the internet.
After I acquired the data science professional certificate in R, I was pumped to look for positions but reality hit! Pandas, seaborn, Tensor Flow, Keras and many Python focused technical skills are listed on most data science job postings along with the annoying oxymoron statement of entry level with 2 to 3 years of experience. Alas! It is time to learn data science using Python! During Kaggle’s data science CareerCon 2019, I learned about the data science fellowship program in DC at Flatiron School. After reading over the syllabus, I was 100% sold on the idea of applying for the fellowship. Not only is the program Python focused, it is also project oriented. Currently, it is a couple weeks into the curriculum, I am thoroughly enjoying the flatiron experience and I am hopeful for my future as a data scientist.
In summary, life is a process of self-discovery and self-improvement. After some detours and introspection, I realized the data scientist is always in me. I am very optimistic about my future career as a data scientist.
