Leaping into Data Science
I’ve always considered myself someone who loves to learn and have had multiple careers that varied greatly from one another. From library assistant, seafood monger, to hearing aid secretary, I’d like to think I can thrive in almost any environment. However, there was one aspect that I had not delved into: data science.
Data science can be explained in a lot of different ways but to put in the simplest way possible, it is the study of data. You’re probably thinking, “That’s it? That can’t be it!” and you’re partially right. Data science can (definitely) get more complicated but we’ll get more into that in later posts. This is just the beginning, after all. Just to expand on this subject a bit more though, the people who work in data science are sometimes called data scientists.
Data scientists are people who organize and analyze data in a variety of ways to generate insights. And this is where I come in. I had been at my job for around eight years, jumping from department to department to see what kind of new skills I could learn. Then one night, googling like a madwoman, I stumbled across some articles that discussed data science and how they are relevant to our every day lives. Data science is something that can be applied to almost everything; from tracking shark migration patterns to even stock market prices. There are certain processes and algorithms that can be used to understand as well as predict outcomes to different data sets. From that moment on, I was hooked.
Diving deeper into the internet, I started learned more about who these data scientists were and what they were capable of. I wanted to be part of the cool kid club too, so like any other person growing up on the internet, I asked the question: How to become a data scientist? Well, good news and bad news. Good news is that you can become one quite easily if you’re dedicated enough. There are a multitude of options to choose from to learn; college courses, online boot camps and even free websites that teach the basics of data science. Bad news is that learning these new skills will take time and if you’re impatient like me, you are supremely bummed out by this. As I stated before, I love to learn but I already want to be good at the things I learned yesterday.
Luckily, once you get started on your data science journey, it doesn’t feel tedious to keep learning new things. In fact, it becomes a bit addicting in that way since the techniques you learn one day become building blocks for projects down the line. I began training myself with instructions from a few free websites, YouTube and even joined the Flatiron boot camp! If you are reading this article and wondering if you should start your data science journey, my advice? Go for it!
After learning some fundamentals, I was able to create my very own data science project! With this made up scenario, I answered the hypothetical question of which movies Microsoft may want to make if they were to join the movie making scene. If you are interested in how I did so, you can check out my github here, where I go into detail in how I accomplished this:
https://github.com/JacquelineFlanigan/microsoft-movie-insights
Stay tuned for further posts in where I will document my ongoing projects.
