Aspiring Data Scientist with knowledge of Python, SQL and Microsoft Power BI. Good understanding & practical knowledge of Statistical Analysis, Data Exploration & Data Cleaning using libraries like Pandas, NumPy, SciPy, Stats Model, Matplotlib & Seaborn. Good practical knowledge of Supervised Machine Learning algorithms using Scikit-Learn (SkLearn) like Linear Regression, Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees, Random Forest and Boosting techniques. Also, good understanding of Unsupervised Learning techniques like K Means Clustering, Hierarchical Clustering, etc. Well versed with the concepts of Feature Engineering, Feature Selection, Feature Scaling concepts along with Optimization Techniques like Re-Sampling (Over Sampling & Under Sampling), Hyper Parameter Tuning using K Fold Cross Validation, Grid Search CV & Randomized Search CV. Good knowledge of ETL concepts using MS SQL Server Management (MS SQL) & Microsoft Power BI. Also, having practical knowledge of manipulating & querying data using advanced SQL queries. Well versed with cleaning and preparing data using Power Query for Data Visualization & Dashboard building in Power BI. Actively looking for roles in Data Science.