Marcel Tiono

• Master of Science - MS, Mathematics with Data Science Concentration.
• Data Scientist: Data Extraction, Data Cleaning, Exploratory Data Analysis, Machine learning with R, Python, Excel (Power query, Pivot Table).
• Data visualization including dashboarding, reporting R, shiny, Excel, Tableau.
• Machine Learning Algorithm including Linear and Logistic Regression, Gradient Boosting (XGBoost, Light GBM), Random Forest, Neural Network.
• Data Analysis, Data Engineering, Data Interpretation, Data Mining using R, Python and SQL, Jupyter Notebook, Microsoft Power Query.

SQL for Data Science (IBM) - Edx

Using Random Forest to Learn Imbalanced Data, Read the data, analyzed data, Sampled data, built random forest models., Constructed random forest models using different cut-off to balance the data (Oversampling, underdamping, hybrid), weighted methods and Smote sampling., Reported and presented to class. Predicting the Supercomputer Usage for NSF Awarded Projects, Extracted data, Explore to detect patterns, Transformed data and Implemented., Constructed machine learning algorithms (XGBoost, Light GBM, MLM, Random Forest and Elastic Net Regression models) to predict the usage rate of supercomputer, Tuned Parameters, Selected the best methods., Posted to Kaggle for competition between student (3rd place). Analysis of Walmart retail goods sales, Extracted, Cleaned, transformed sales data for analysis using R studio., Performed data visualization of the sales to identify patterns, trends and outliers., Built different machine learning and times series models to predict twenty-eight days forecast of item sale in various location.