My Learning Plan for Getting Into Data Science from Scratch
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. The gap was clearly my lack of knowledge in 1) coding and 2) machine learning mode
