Jul 25, 2020
Since a young age, I have always taken an interest in programming in general. I grew up around technology, my father is a programmer. I remember asking him for a programming book back when I was in junior high school. I picked up Pascal as my first programming language and I was delighted with this new tool that allowed me to write code and make the computer do new things!
Ever since that first feeling, I have known that I would like to pursue Computer Science. So when it came the time to decide what I would study at university, I enrolled in a Computer Science degree. I began learning the foundations of many topics such as mathematics, data structures, databases, theory of computation, software engineering amongst others. During the course, I have learned a few widely used programming languages like C, Java and Python but also OCaml for functional programming. At the end of this degree, I had the opportunity to take part in academic research for a couple of years. It was the moment when I started to explore the data science world for the first time.
During these first academic research years, I started to learn how data can play an important role in various domain fields and exploring data and studying it can lead to very interesting knowledge insights. This was an incredible concept for me and I decided it was the right time for me to invest into a graduate degree so I started a Master's degree focused on machine learning and distributed systems.
During my Master's study, I was introduced to the mathematics concepts behind machine learning algorithms, tried out different approaches from supervised learning, unsupervised learning and reinforcement learning and had the chance to apply them in some academic projects (some available on my GitHub). While attending this course, I continued to work on academic research and built a project called GreenHub that studies energy consumption in the context of mobile devices, it generated a dataset with an impressive number of 45 million records. This work was very important to get my feet wet in the process of defining and designing a data science experiment and practising a variety of tasks from extensive data cleaning to exploratory data analysis and at a later stage statistical modelling as well.
Now I am close to finishing my Master's degree. I feel like my learning path of data science barely started and there are so many fascinating new use cases where different industries can benefit from the power of data. I think this video, by Cassie Kozyrkov, beautifully explains applied machine learning and the "potential of turning information into better action" to quote her.
So as I take my first steps into this new career, I came upon this piece of advice, to start blogging about data science and document my learning journey. Where I will explore new topics and algorithms, practice and develop my skills and track my progress over time. I feel very lucky to be doing what I love, and I am very excited to see how the world will change!