My Data Science Journey:

Installment 1:

For me it was easy to fall in love with Data Science. I have been fascinated with Math, Science, Computers, and Engineering since a very young age. I've always enjoyed measuring and recording data. Games where characters had a large number of statistics recorded for them have always been interesting to me. I spent a lot of time exploring many career paths. When I turned 27 I purchased myself a computer as a birthday present. At that time I had decided I was going to learn how to program for iOS app development. I spent a few weeks exploring XCode, Swift, and many lessons for basics apps to make. My free learning resources started to become more complicated and the amount of time I had to dedicate to learning wasn't a lot. I started exploring a few other course and eventually stumbled across the Python language. Shortly after that I found youtube videos that explored Python from a strictly Data Science Perspective. I started building Machine Learning Models from tutorials online. Youtube, Kaggle, GoogleAI, just to name a few. I was hooked.

Every second I wasn't working was spent learning the theories behind Neural Networks and running through the code to do it. I hit a lot of road blocks due to free learning sources being limited, focused, and requiring prior knowledge I didn't have. After months of exploring the free resources and hitting every wall imaginable I decided to enroll in General Assembly's Data Science Immersion program. I had heard about it maybe two weeks prior to deciding to enroll when I attended a free HTML CSS workshop they hosted on there campus. When I realized they would accept my GI Bill I enrolled. The on-boarding process required me to analyze a dataset for KickStarter and answer questions about it. My Python skills were still weak since I had been working only with pre-cleaned data and tutorials that laid most of the code out for me. I had only briefly used Excel in High School however, I was able to figure out how to use it from google much quicker than I was able to answer my programming questions.

After a little less than two days of Data Exploration and building presentations I came in for my acceptance interview. I had to go over my analysis and briefly explain the presentation I had made. Thankfully I was enrolled. One of the biggest benefits of this was I could now dedicate my entire week to learning Data Science and actually learn to use the tools that I've found to be much more efficient. I'm currently writing this article during week 6 of the 12 week course. During this time I have learned so much about Data Science, Statistics and, Programming it's been incredible. While it is important to have a large arsenal of tools (Machine Learning Models, Programming Languages, Database and Cloud computing skills) I've learned that understanding the Statistics, Math and, Feature Selection is equally important if not more-so.

Data Science is useful because it allows us to implement powerful tools to predict data in highly accurate ways. This is already useful at this step alone. Imagine you're programming a self-driving car. It needs to be able to accurately recognize pedestrians, traffic lights, other cars, animals and, how all of those things are moving. We can feed this information into programs in order to make the car behave a certain way in the presence of all of these variables. However, true Data Science goes much further. In many cases we will want to use data to predict an outcome. If we train a model to accurately predict an outcome we can determine which variables affect the outcome the most. This allows us to know what features increase the value of a home the most or improve prediction rates on stock prices and really help answer any question as long as there is a sufficient amount of answered data.

On top of all of these already amazing things. The advent of neural networks has given us great insight into understanding more about how our own brain works. By training computers to understand images and classify them we have gained an understanding as to how we may do that as well. We've been able to apply advanced mathematical concepts that couldn't be utilized before due to new emerging technologies.

I've only just begun the process of truly learning Data Science and there much more math and programming that I will need to learn in order to do so. But, I've never been more excited about a field in my life. If you enjoy puzzles and understanding how the world works as much as I do than Data Science may be a great field for you too.