My decision to get into data science started way back when I was still in college in early 2015. I actually didn’t plan to become a data scientist originally, but a quant — someone who is essentially a financial analyst that uses advanced math and coding in their functions (e.g. risk management and algorithmic trading); however, a 9-month quant internship made me realize that I wanted to apply these skills to a wider context.
A few blog post readings later, I concluded that data science was the field for me. Coming from a background in Applied Economics, I felt my econometrics heavy curriculum already gave me a decent foundation for the math; however, I still had no background in the models used in machine learning (e.g. neural networks, random forests). In addition, I looked through the courses for the rest of my stay in university and found nothing that taught us how to code our own algorithms.
Author: Lorenzo Ampil
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