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Data Analytics, SQL, Pthon, Big Data, Machine Learning

Boston, Massachusetts, United States
August 29, 2018

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RIJUTA WAGH 617-***-**** LinkedIn Tableau Public Github

**** ******** **, *** **, Boston, MA - 02215


MS Information Systems: Northeastern University, Boston, USA Aug 2018 Coursework: Data Warehousing and Business Intelligence, Data Science, Data Analysis using Python, Big Data Technologies BE Computer Engineering: University of Mumbai, Mumbai, India May 2016 SKILLS

• Programming: R 3.3.2, Python 3.5, Java, SQL, PLSQL

• Tools & IDEs: Jupyter Notebook, RStudio 1.0.136, Eclipse, Toad data Modeler 6.0, Git 2.11.0, Rational Rose, SQL Server Management Studio, Microsoft Azure Machine Learning Studio, UMLet, Jira

• Data Technologies: MySQL, Oracle 11g, Postgre, Apache Hadoop, MapReduce, Hive, Apache Spark, Databricks

• BI and ETL tools: Tableau, PowerBI, Talend, Qlik Sense

• SDLC Methodologies: Waterfall, Agile


Business Intelligence Analyst Co-op: Fidelity Investments Jan-June 2018 Technologies used: Qlik Sense, Tableau, Oracle

• Analyzed investor branch production, cash management strategies using various business metrics to detect anomalies

• Created intuitive visual representations in Tableau to provide actionable insights to financial stakeholders

• Gathered knowledge about the PI Sensors data model to write efficient queries to reduce report processing time by 50% in Qlik Sense

• Developed SQL queries for various KPIs impacting the business performance and generated visualizations for various monthly and yearly financial reports in Tableau and QlikSense ACADEMIC PROJECTS

Contosa Retail Data Warehouse & Business Intelligence May-Aug 2017 Technologies used: Talend, Qlik Sense, Power BI, Tableau

• Configured ETL activities for a Retail data warehouse in Talend, also improved the performance of these jobs by 80%

• Proposed a solution to implement slow changing dimensions to track the changing product pricing and costing in a retail warehouse that made more data available for analysis

• Created interactive and elegant dashboards using Tableau, PowerBI, QlikSense to answer the business queries Data Science, Lending Club Loan Dataset Jan-March 2017 Technologies used: Python (numpy, pandas, matplotlib/plotly), R, PowerBI

• Preprocessed data and performed exploratory data analysis using PowerBI and Python

• Built classification and prediction models using Linear Regression, Logistic Regression, Decision trees, Random Forest and Neural Network models that generates a flag to determine whether a person should be granted a loan or not

• Segmented data into clusters using K-means clustering based on various features and built prediction models to predict interest rate for each cluster

Sentiment Analysis of Amazon Fine Food Reviews Mar-April 2017 Technologies used: Python, R, PowerBI, Microsoft Azure Machine Learning Studio, Amazon Web Service, NLTK

• Automated web scraping to download data for amazon fine food reviews and preprocessed it using tokenization and stemming

• Classified the reviews as positive, negative and neutral using classification algorithms such as Naïve Bayes, Logistic Regression and Decision trees

• Deployed and implemented Logistic Regression model on Microsoft azure machine learning studio Analysis of Yelp dataset Aug-Dec 2017

Technologies used: Apache Hadoop, Hive

• Analyzed Yelp data on various business attributes such as top 5 businesses by category and state, aggregated check-ins over time, time series analysis of user’s ratings, sentiment analysis using various Hadoop MapReduce patterns and Hive ACHIVEMENTS (Business Intelligence Tool used: Tableau)

• Winner of INFORMS Data Visualization Hackathon held at Northeastern University April 2018

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