Favorite quote: “A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.” ~ Robert Anson Heinlein Skills: • Version control with Git • Product Management • Data Analysis Methodology • Data Ecosystems: data repositories, sources, formats, types, pipelines, roles... • Data Science Methodology • SQL • Database Structures • Relational Databases: PostgreSQL, My SQL • Python • Webscraping with BeautifulSoup • Scientific Computing Python libraries such as Pandas, Numpy, Scipy • Data Visualization: Matplotlib, Seaborn, Folium, Plotly, Spreadsheets, IBM Cognos Analytics, Logi Composer... • Supervised and unsupervised machine learning models (Python Scikit-Learn library) • Regularized regression algorithms: Ridge Regression, LASSO Regression, Elastic Net • Classification algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees, Bagging, Random Forests, Extra Trees Classifier, Boosting, Stacking... • Unsupervised Learning: Clustering and Recommender Systems • Clustering Algorithms: K-Means, Hierarchical Agglomerative Clustering, DBSCAN, Mean Shift. • Dimensionality Reduction algorithms: Principal Components Analysis (PCA), Kernel PCA, Multi Dimensional Scaling, Non-negative Matrix Factorization • Exploratory Data Analysis techniques • Feature Engineering & Variable Transformations: BoxCox, Polynomial, Encoding, Scaling... • Hypothesis Testing Check Featured section for my Data Science projects.
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