Scientist in love with data analysis and modeling. I love asking myself questions and continually learning. This has led me to develop models that answer my interrogations. I enjoy telling stories through science. My interests are Quantum Computing, Machine Learning and Data Science.
My toolbox: SQL Python Numpy Seaborn Matplotlib Pandas PowerBI Scikit-learn Jupyter Lab Tensorflow
Lenguages: Spanish(Native), English(B2) , Portuguese (A2)
Development of machine learning models for applications in quantum computing.
Logistic regression, random forest and recurrent neural network.
I had the opportunity to guide and lead dozens of students in the development of their basic experimentation skills. To maximize learning processes, everything was done ensuring their safety during the lab and promoting a work environment of constant communication.
For 4 years in January and February I gave chemistry lectures to hundreds of freshman students. The project consisted of a chemistry pre-course whose objective is to prepare the student to face their first General Chemistry course at university.
My job consisted of answering questions about chemistry and physics from dozens of students. The review classes were attended by 30 people per group. We impact and collaborate in the student's training process.
I focused on the area of computational physics and materials science.
Computational Techniques: Linux Git Python C
I developed projects for calculating uncertainties via Monte Carlo simulation or by estimating uncertainties by the analytical methods for different physical models.
In this period I focused on data science and machine learning by taking various online courses. Also I learned how to use and integrate Pandas and SQL for data analysis by carrying out projects that seek to answer questions from physics or from everyday life.
Research: Our research explores quantum error correction using LSTM neural networks.
In this period I developed my experimental and modeling skills.
Lab Techniques: Volumetric Analysis Instrumental Analysis Gas Chromatography HPLC UV-vis Spectroscopy Flame Atomic Absorption Spectroscopy Air-Sensitive Techniques
Linear modeling: I also developed my abilities in linear and polynomial modeling to explain physicochemical phenomena. Here I fell in love with data management.
Leadership, project planning and service skills: I was an active member of our school’s student committee as a former vice president. Among my favorite projects is the Chemical Show, a show of experiments that sought to motivate children to study science and engineering careers. We were able to reach more than 2000 children from various parts of the country. On the other hand, I founded the Laboratory Induction, a project whose objective was to prepare students for their first laboratory. A total success, more than 5000 beneficiaries improved their experience. We concluded our period by founding the first student chapter of the American Chemical Society in Costa Rica.
A repository that contains a series of data science projects and datasets. Pandas, numpy and matplotlib are used as the main analysis tools.
Introducing Python to chemistry students. Numpy, Pandas and Matplotlib for chemical calculations and linear modeling.
We use neural networks to correct quantum errors in simulations under different error models.
Python C
Data Manipulation Exploratory Data Analysis Data Visualization Supervised Learning Unsupervised Learning Deep Learning with Keras-Tensorflow Statistical Thinking
Pandas NumPy Scikit-learn Matplotlib Seaborn PowerBI Tensorflow Jupyter Notebooks Spidey SQL SQL-Server Apache Impala Git
Windows GNU Linux
Hard-Working Highly Adaptable Creative Project management Confidant Responsible Reliable Team leadership
I learned the big data SQL engines Apache Hive and Apache Impala, but most of the information is applicable to SQL with traditional RDBMs as well; the instructor explicitly addresses differences for MySQL and PostgreSQL.
This course was about the Data Science process in Python. I learned how to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher.
Five courses about the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Topics coverered are Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches applied machine learning skills with TensorFlow and how to build and train powerful models.
A 5-course certificate program. I was prepared for an entry-level job in IT support through an innovative curriculum developed by Google.
Mentor in the Tunnel of Science in Costa Rica. More than 20 thousand people discovered the secrets of the cosmos, the complexity, energy supplies and materials in this exciting journey.
I worked on developing computational thinking in community children using code.org as a platform. I also mentored mothers who wanted to advance in their high school studies.