The Reason I Started Data Science as a Business-Student — 1
It has been almost a year that I’ve been studying data science. It feels like I finally get the hang of it, thanks to several projects I’ve been doing so far. However, the meaning of data science sounds too broad and ambiguous, so I want to help you understand what it is at first.
As some of you might know, data science includes everything from simple work in Microsoft Excel, programming work for data mining, building database, preprocessing data from a database to feed them into machine learning models, and all the way to building machine learning or deep learning models.
I know it’s still too wordy, so let me give you a simple analogy for them. Data science is like cooking, and you can think of each of data mining, storing data to a database, preprocessing data, a machine learning process is shopping grocery, putting grocery into a fridge, preparing ingredients, cooking with prepared ingredients respectively.
If you are not familiar with data science, it will be hard to make sense out of it, and that’s what I just felt a year ago.
A year ago, I knew nothing about data science and even the name of it. All I knew were the terms like ‘AI’, ‘machine learning’, ‘big data’. Nothing more than that. At the time, I just finished military service (it’s mandatory for men in Korea) and I was in my junior year of University, and I didn’t try to learn those things because I thought it was too late to learn those cutting-edge technologies as a business-student. Then, as most students with non-technical majors do, I tried to find something that I can do well, just pressed for anxiety for the future. The answer I found was, not so surprisingly, English. (I’m a Korean.) After that, I started to put all my energy into learning English to reach the point where I can confidently say, ‘I’m good at English’, and my ultimate goal became to be able to use English like a bilingual.
Surprisingly, that was my starting point of a journey for data science.
There are tons of stories to tell about learning English, but I will try to make it short here. First, I tried to learn English, not study. I rarely used workbooks for TOEIC or TOEFL (they are sorts of English tests in Korea), but rather, I tried to learn English as if I was an infant. Infants never learn their mother tongue with workbooks and language tests. They naturally learn it by listening, reading, speaking, writing as much as they can. Therefore, I think it’s not that different to learn a language for an adult as well. I chose an E-book from Amazon and read through it slowly while jotting down every expression and vocabulary that I didn’t know. It was harsh. It took 4 months to finish while also doing school work. When I read it again, it took a month, and on the third try, it took less than a week to read through. After I absorbed it with my eyes, I bought an audiobook for it and read it again with my ears. On a subway commuting to school, I watched CNN10 on Youtube and jotted down everything that I missed from listening.
Even now, I have a habit of adding a word into my wordbook immediately when I encounter a word that I don’t know. After half a year, the number of saved vocabulary has reached about 5,000, and now, the number still stays around 5,000, which means most English expressions can be covered by those words I already learned. So, I applied for the TOEIC test, which is a common English test in Korea, to get a score for my future resume. After a couple of weeks, I got a result from the test, and the score was 960 out of 990. It was a great score considering it was my first shot, but I wasn’t that happy. ‘What does it matter, if I can’t use English like a native speaker?’, ‘How can I really see whether I’m good at English or not?’ those kinds of thoughts filled my mind after the test. Then, a great idea came up. That was ‘If I can learn something in English, wouldn’t that be proof of my English fluency?’ So, I went to Coursera and made up my mind to give it a shot. My target was a machine learning course because it was both free of charge and hard enough to test my English skills. I thought it would be proof of my English skills if I could manage to finish the course, and at the same time, it would be a great opportunity to see what machine learning is because every media had been talking about AI, machine learning, and those kinds of things.
That machine learning course has changed my point of view for everything. It was not just because of the machine learning itself. It was because I finally realized why I should learn English and how many things I can do with English, and that’s what shook me to the core. The quality and learning system of Coursera were far much superior than that of Korean platform or even a offline course from my University. I was a student of a decent University located in Seoul, and was paying a lot, but it felt like most courses of my University were just scraping the surface and copy-and-paste from textbook. At the time, I thought it was a natural thing, given that one professor had to deal with a host of students. However, I found it wasn’t.
As I gradually understood the concept of machine learning through the lectures by submitting coding assignments and getting feedback, and sharing thoughts with a lot of students around the world, I found it was possible to explain such a highly ambiguous things so easily, and found learning online can be highly effective. Therefore, the next thought that came up was ‘It would be such a shame if you can’t get an opportunity to learn just because you don’t know English.’ That changed my point of view for English. Before then, I just thought English of an ultimate goal, but since then, English became a tool for me to broaden my perspectives.
Let’s say you found a magic wand which can create money by magic. Will you make money by selling it or using it?
Korean version: 찰리의 늦둥이 블로그 :: 평범한 경영대 학생이 데이터 사이언스를 시작한 이유 — 1 (tistory.com)
