My main passion since I was 15 y/o is to code in Python and learn how to develop my problem-solving skills within a certain logic. Python has changed my life, in a such way that now I'm persuing my dreams as a Computer Science Engineer and Cybersecutiry specialist. I also love to study and learn from many social idealogical topics, in order to acquire better global skills. This has brought me to become a podcaster since 2019, giving me the opportunity to communicate my own point of view with professional guests in my show, demonstrating another way to see the world. Currently I am studying at CEBI, an IB school that impregnates the Open-minded, Thinker, Inquirer, Communicator, Risk-taker, Caring, Principled, Knowledgeable, Well-Balanced and Reflective skills into their students. Thanks to this, I could stablish social connections with my local community, to impulse a betterment within the environment, human-care, informational content and learning fundamentals while being under the COVID-19 World Pandemic. Also, I have the highest grades of my class, showing my commitment for learning and being responsible with my social surroundings.
Communicator / Podcaster
Cybersecurity with Python
Livestreaming With OBS - Church Service
In this project I build a Machine Learning Algorithm in order to predict how many planets can a certain system have based on its mass, age, etc. It was a very interesting project built with python and Decision Tree Algorithmic aplication, but it can also be applied with polinomial models.
This project is a personal boosted idea that I decided to implement since 2019 to the present date. I see this podcast as an excellent way to communicate my ideas, learn from professional people in the topic, and discuss about environmental issues or give specific information about some of the IB topics. Which has allowed me to develop my communication and learning skills.
This project focuses much more in the data processing using Python, using Data Science concepts to compare big data. Over 24 Thousand data were processed and analyzed using MatplotLib and many other Statistic libraries. Comparing the Air Pollution data before and during the Pandemic of the Covid-19, to stablish environmental conclusions of its impact.