My name is Diego García Mula, I’m 22 years old and am a recent aerospace engineering graduate. In this Bootcamp Diary, I’ll give you an insight into my experience in the IE Data Science Bootcamp.
So far, the Data Science Bootcamp has been a great experience for me. Over the past five weeks, I’ve already learned many new skills within Python programming—much more than I learned in a whole year of my engineering degree. I’ve been very lucky to meet some great classmates from many different cultural and educational backgrounds, with and without work experience, which has made this experience very enjoyable. All the professors we’ve had have been excellent, and the first classes are very enjoyable and interactive, which I think is another great aspect of the course.
Over the past two weeks, and for the upcoming one, I’ve been taking part in the Data Science Bootcamp online. I’ve been in the UK, so I’ve had many days where I’ve had classes from 8 a.m. until 7 p.m. This might sound completely infeasible, but in all honesty, it has been quite easy to follow along with the classes. I even completed a Data Visualization exam in Python. The fact that the unique Liquid Learning format is offered for this Bootcamp is very interesting, and in my opinion, a great idea. It provides great flexibility for anyone considering the Bootcamp, and as I mentioned earlier, the fact that it is so interactive is why it doesn’t feel tedious online, unlike so many other similar programs.
I would also really like to mention the Capstone Project, which, in my opinion, is probably the most attractive aspect of the Bootcamp. Having the opportunity to work on a real-world project with a client who is looking for a real data science solution is the best way to learn how to do data science. I believe that there is no better way to learn and understand how to do something than with actual projects and practical work, so I am very happy regarding this aspect of the program.
I am in the Q-Energy project, for which we need to build a model that can predict the energy market prices for the following day. It’s a very interesting project and my teammates are great to work with. We’ve started working with the data provided to us, and thanks to the help of the Q-Energy team, we’ve been able to start manipulating datasets, creating new columns and variables for data analysis. It has been very satisfying to be able to do this, as in a way, it makes you feel somewhat like a real data scientist already! We’re working really hard for our Exploratory Data Analysis (EDA) presentation next week.