Data Analyst
Resume:
SHEEL VARMA www.linkedin.com/in/sheel-varma-***b**a8
************@*****.***
https://github.com/sheelvarma
SUMMARY
Recent Graduate from Pace University, New York. Actively looking for Entry Level positions in the field of Data Analytics.
EDUCATION
Pace University, Seidenberg School of Computer Science and Information Systems New York, NY
Master of Science (MS) in Information System
GPA: 3.77/4.00 May 2018
Mumbai University, Shah and Anchor Engineering College Mumbai, India
Bachelor of Engineering (BEE) in Information Technology June 2016
TECHNICAL KNOWLEDGE
Database: MySQL, MS SQL Server, NoSQL
Programming Languages: R, Python, Java, C, Spark
BI Tools: Tableau, Looker
Web Technologies: HTML5, CSS, PHP
Office Tools: Microsoft Excel, Microsoft Word, Microsoft PowerPoint, Microsoft Access
Development Tools: RStudio, Jupyter, PyCharm, PHPStorm, Eclipse, NetBeans, Visual Studio 2013
Operating Systems: Windows, Linux
Other Tools: Git, ArcGIS
WORK EXPERIENCE
Sciffer Technologies May 2016-August 2016
Data Analyst Intern
Gained in depth knowledge of advanced R techniques.
Performed data analysis and forecasting on various datasets using Regression Techniques, Hypothesis testing and Time Series Analysis and Classical Statistical Tests.
Implemented various Machine Learning algorithms using Classification and Clustering techniques.
Visualized the data using various graphical techniques like scatterplot, histogram and boxplot.
PROJECTS
Credit Card Default using R (R Programming, Microsoft Excel)
Examined the data for missing values and reconstructed for explanatory data analysis.
Conducted Explanatory Data Analysis using lattice, ggplot2, dplyr for data visualization.
Performed testing and training of the sampled data for predictive data analysis using 80-20 rule.
Implemented KNN and Decision Tree algorithms, mapped and achieved over 90% accuracy.
Sentiment Analysis on Product Reviews Using Hadoop (Apache Hadoop, Java, PHP, MySQL)
Gathered user reviews, stored in the database and reduced processing time for large dataset using Apache Hadoop.
Executed first Map-Reduce using of sentence detection, punctuation, phrase and stop word removal with feature category and parts of speech tagging (POS).
Performed second Map-Reduce with feature category using clustering techniques like Apriori Algorithm and Apache’s OpenNLP was used for Sentence Detection & POS Tagging.
Implemented Word Classifier, SentiWordNet and calculation of overall value on second Map-Reduce.
Analyzed the output using a progress bar to check a positive, negative or neutral sentiment.
Glass Types Classification (Python, Microsoft Excel, Jupyter)
Imported libraries Numpy, Pandas and Seaborn and read the dataset from the csv file.
Rectified the data for missing values, and spilt for testing and training purposes.
Evaluated the results for the glass types using Decision Trees and mapped for accuracy using Confusion Matrix.
Improved the result by 25% using Random Forrest Classifier and mapped for accuracy.
Movie Recommendation System (Jupyter Notebook, Python, Recommendation System)
Analyzed various datasets, merged and performed explanatory data analysis.
Examined the variables using seaborn and matplotlib libraries for data visualization.
Constructed pivot tables for the required variables and validated the data for flawed values.
Interpreted correlation analysis using threshold values and displayed the recommended movies.
CERTIFICATIONS
Tableau 10 for Data Science – Udemy.com
Python for Data Science for Essential Training - LinkedIn
PUBLICATIONS
International Journal of Computer Applications: http://www.ijcaonline.org/archives/volume142/number11/24942-201-***-****
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