I was "trained" in the culture of data modeling. I don't think it could have been any other way, because the concept was to be a financial researcher from a young age, with the goal of extracting knowledge about the nature of the relationship between response variables and explanatory variables.

So, over the last two decades, I've evaluated hundreds (yes, hundreds of articles and consulting reports!) of databases using classical statistical approaches such as Descriptive Analysis, Bivariate Analysis, ANOVA, Regression Analysis, Multivariate Analysis, Generalized Linear Model, Generalized Estimating Equation, Generalized Mixed Model, Time Series Analysis, Structural Equation Modeling, Spatial Analysis, Item Response Theory, and Computational Simulation.

The Econometrics discipline was my foundation for the first 15 years; however, in the last five years, I have focused on the Psychometrics discipline, due to a high demand to work with latent variables and validate measurement instruments, and, of course, because I have fallen in love with psychometrics techniques. Anyway, I've always worked with health experts during this time, so I'm comfortable with their procedures and language. In this context, I attempted to master a variety of proprietary software, including Excel, SPSS, Stata, Eviews, Amos, SmartPLS, and Statistica, as well as open-source software, including R, JASP, jamovi, GPower, and GeoDa.

However, in recent years, especially after 2020, I have focused my efforts on algorithmic modeling culture in order to work with massive databases and focus on prediction. As a result, I have worked to improve my understanding of the following machine learning techniques: Lasso and Ridge Regressions, KNN, Random Forests, Bagging, Boosting, Neural Networks, and Support Vector Machines (SVM). Other subjects in this culture (Linear Regression, Logistics and Stepwise, Decision Trees, Discriminant Analysis, Cluster Analysis, and Principal Components Analysis) are familiar to me because they are already widely used in the data modeling culture, and I have worked on them in dozens of situations.

In this context, I have attempted to study the most popular Data Science tools, including SQL, R, Python (numpy, pandas, matplotlib, seaborn, statsmodels, scipy, scikit-learn, and so on), PowerBi, and Tableau. I've used RStudio and Jupyter as IDEs within the Anaconda environment.

• 🌍  I'm based in Uberlândia, Minas Gerais, Brazil
• 🖥️  See my portfolio at Personal Website
• 🚀  I'm currently working on Educational materials for data analysts and scientists
• 🧠  I'm learning Advanced Machine Leaning in Python and R
• 🤝  I'm open to collaborating on Anything related to data science and data analysis

and much more... see on my Website

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