As a data scientist, I have extensive experience with advanced data analytics projects, including classification, regression, clustering, and more. I have successfully managed diverse data types, such as event transactional data, machine sensor data, and engineering data.In my role, I engage in a wide range of activities that span the entire data science lifecycle. This includes understanding business needs, coordinating resources, data wrangling, feature engineering, developing and validating models, visualizing insights, and ultimately deploying end-to-end models into production to drive crucial business decisions. I understand and effectively utilize appropriate algorithms and tools to solve business problems.Skill Set* Machine Learning Algorithms : Classification (e.g. Support Vector Machines (SVM), Logistic Regression, Decision tree, Ensemble (Random Forest, xgboost, Isolated Forest)), Regression (e.g. Support Vector Regression (SVR), Neural Network), Clustering (e.g. K-Mean & DBSCAN), Feature analysis (e.g. Principal Component Analysis (PCA)), Dynamic Time Warping (DTW)* Simulation : WITNESS and Flexsim for Discrete-event simulation* Optimization : Coopr / Pyomo (similar to AMPL) to formulate linear programming models and use cbc and Gurobi (similar to CPLEX) to solve the problems* Programming : Python (Scikit-learn, SciPy, NumPy, pandas, impyla, gh5), VB.NET, C#, Java* Visualization : Tableau, Python (matplotlib, seaborn) * Data Wrangling : MS SQL Server, Oracle, MySQL, Hadoop using HiveQL, HDF5 (Hierarchical Data Format 5)