Source: Deep Learning on Medium
In 2017, during the last year of my master degree program, I accidentally picked a project where I was exposed with Big Data. To be more specific, I selected a topic of benchmarking database performance of Internet of Things datasets. I was playing around with GraphQL and Virtuoso. It was the moment where Data Science topic became more familiar in my life. I looked up at Youtube just to get the idea what is Data Science. The video explained about making future prediction based on datasets. It brought up the courses of Statistics and Probabilities that I had taken during my Bachelor of Computer Science. So basically, I thought that Data Science is Computerised version of Statistic and Probabilities? Trust me, even until today, I still have not figured out the solid definition and the scope of Data Science.
My journey studying Data Science started when I enrolled to Udemy course by Jose Portilla. At the beginning I was really worried that I didn’t have sufficient knowledge of Mathematics (such as Linear Algebra, Calculus, Integral, Derivatives, etc). All regrets for not studying hard enough on during my bachelor started to haunt me, haha. Anyway, when studying the course, Jose explained it very comprehensively. I would highly recommend you taking the course if you want to touch the base of what Data Science is. And the mathematics part somehow have been managed my Python Libraries, so I believe it is about how we use the library to get the result. The most interesting part about Data Science is actually the solving problem part in real-life cases.
After getting myself familiar with Data Science term and python’s libraries, I kinda took a break from studying Data Science. I was hired to work as Front-end developer, my daily routine pretty much just playing around with React JS. Throughout the time, my curiosity kicked back. I was more curious than ever. I decided to speak to a friend who just recently move to Melbourne to do PhD in Deep Learning. After speaking to him, I found it a bit funny that he was also unsure about the definition and scope of Data Science. After having a discussion with him, he suggested me to enrol to Andrew Ng’s Deep Learning course at Coursera. Then, I started to do my research about the course. Throughout the process, I was introduced to the subsets of Data Science (please correct me if I’m wrong) such as Artificial Intelligence, Machine Learning, and Deep Learning. I was overwhelmed with the definition. Until I found the picture which kinda explained the whole thing.
To be honest, I’m still in the process of figuring out of everything. I once joined Data Science event in Melbourne, where they discussed about Imposter Syndrome of Data Scientist . I would personally suggest to not worry too much about it. As long as you love playing around with data, and you know what to do with it, then, I believe you are entitled to call yourself Data Scientist. Personally, I want go further into Deep Learning. Getting the certificate from Andrews’s course would be my short-term goal.
“ When you hear the term deep learning, just think of a large deep neural net. Deep refers to the number of layers typically and so this kind of the popular term that’s been adopted in the press. I think of them as deep neural networks generally”.
In the next post, I will demonstrate a bit of Machine Learning cases. In case you have not installed Jupyter Notebook and Python in computer, it’s time to get it and play around.