Skills
Languages: Python, R, JavaScript
I am most proficient in Python and I consistently use it in my projects. I am also familiar with R and JavaScript. In regard to Computer Science, I have a very good understanding of data structures, searching and sorting algorithms and the concepts of time and space complexities.
Data Cleaning: Pandas, NumPy
During a data science project, I spend most of the time in cleaning and organizing data. I constantly use Pandas and NumPy for those stages. Those tools become also handy when I do my initial investigation on the data. It also helps me discover patterns, test hypothesis and check for assumptions with the help of summary statistics and graphical representations.
Data Visualization: Seaborn, Matplotlib, Plotly
Data visualization is one part of the data science project life cycle that I enjoy a lot. I am experienced in using Seaborn, Matplotlib, and Plotly. I am also a big fan of Geoplot and Folium, which I have used in my work. When I write code in R, I feel most comfortable using Ggplot.
Machine Learning: Scikit-Learn, TensorFlow
In the matter of machine learning models, I am most comfortable working with Scikit-Learn. I enjoy working with Scikit-Learn a lot, since I have a good understanding of most of their algorithms. I have also used it in my previous projects. I am also familiar working with TensorFlow, when working on problems that consist of neural networks.
Databases: SQL, MongoDB
In regards to databases I am most trained in SQL and MongoDB. In the past, I was also exposed to Google's Firebase, PostgreSQL and Sequelize.
Version Control Systems: Git, GitHub
When I begin a new project, I make sure to plan accordingly and organize myself in the best possible way. For that, version control tools are a must, I use Git and GitHub on a regular basis.