Data Analyst

Virinchi Ande

**************@*****.*** 980-***-**** 12002 Diploma Dr. Apt M, Charlotte, North Carolina 28262 GitHub LinkedIn SUMMARY

Qualifications 2 years of industry experience

• Experienced as a Data Analyst using Python, R, SQL, T-SQL, Tableau, and Machine Learning Algorithms EDUCATION

Master’s in Computer Science, University of North Carolina at Charlotte GPA: 3.87 December 2017 Bachelor’s in Computer Science, Vellore Institute of Technology May 2014 COURSES

Machine Learning Cloud Computing for Data Analysis Data Warehousing Database Systems Knowledge Discovery in Databases Data Mining SKILLS

Development Python (Matplotlib, Pandas, Numpy, Scikit-learn, Seaborn, Jupyter Notebook) R (Mice, SQLDF, ggplot2, klar, devtools, rpart, arules, caret) Java, C, C++, JavaScript, HTML, CSS (Bootstrap), MATLAB Data Apache (Hadoop, Spark), Microsoft BI Stack, Google Analytics Machine Learning Modelling, Pre-processing, Regularization, Ensemble Learning, Regression, Classification Statistical Analysis, Evaluation, Fine-Tuning

Databases SQL (MySQL, PostgreSQL, Oracle, PL/SQL, SQL Server, Azure), NoSQL (MongoDB) QA White-Box, Black-Box, Grey Box, Unit, Integration, Smoke, Data Driven Tools Apache (Tomcat), Tableau, Qlikview, WEKA, MATLAB, SSIS, SSAS, SSRS, Visual Studio Oracle SQL Developer, TOAD, Microsoft (Excel)

EXPERIENCE

Data Analyst at Accenture Python, Excel, SQL, SSIS, Tableau, R, RandomForestRegressor 07/14 - 07/16

• Reduced dataset dimensionality by performing Principal Component Analysis using Python and R

• Improved prediction accuracy by 10% implementing a regression algorithm called RandomForestRegressor

• Achieved a recall rate of 93% by building a model to determine fraud transactions using SVM classifications

• Predicted house prices by developing a predictive analytics platform using Python and Tableau

• Rectified a defect by correcting the logic in 2 procedures and 6 functions using Dynamic SQL and queries PROJECTS

Income Prediction GitHub Python, Matplotlib, Pandas, Jupyter Notebook 12/17 – 1/18

• Achieved 3.1890 RMSE value by using Ensemble Learning. Placed 17th in leaderboard among 570 participants Housing SalePrice Prediction GitHub Python, Seaborn, Jupyter Notebook 11/17 – 12/17

• Achieved 0.0619 RMSE value by using XGBoost. Placed in top 10% in the leaderboard Human Resources Analytics GitHub Python, IPython, Jupyter Notebook, Matplotlib 08/17 - 09/17

• Achieved 97% accuracy by implementing Random Forest Classifier using Python Hospital Management System GitHub MySQL, SSIS, ETL 02/17 - 04/17

• Merged 3 independent entities into a data warehouse using Snow-flake schema PokemonGO Data Analysis GitHub Python (PySpark) 01/17 - 04/17

• Predicted Pokemon spawning location based on 3 attributes using K-means and Naïve Bayes Nursing Home Compare GitHub R, Python, WEKA, Tableau 01/17 - 04/17

• Predicted the rating by implementing Decision Tree and Neural Networks using R, Python and Tableau

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