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Data Analyst Intern Resume

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Houston, TX


  • looking for a position in Data Science domain with hands - on approach to tackling projects and accomplishing goals.
  • Passionate about gleaning insightful information from data assets and developing a culture of sound, data-driven decision making.
  • Experienced with machine learning algorithm such as logistic regression, KNN, SVM, random forest, neural network, linear regression, lasso regression and k-means


Programming language: HTML, CSS, C, C++, R, Python (NumPy, SciPy, Pandas), SAS, Java, Ajax, Perl, MATLAB, Scala

Business Intelligence tools: Tableau, OBIEE, Splunk, SAP Business Objects, QlikView

Development Tools: Microsoft SQL studio, IntelliJ, Eclipse

Databases: NO SQL, PostgreSQL, MySQL, Microsoft SQL Server 2008

Reporting Tools: MS Office (Word/Excel/Power Point/Outlook), SSRS


Confidential, Houston, TX

Data Analyst Intern


  • Data Analysis-Data collection, data transformation and data loading the data using different ETL systems like SSIS and Informatica.
  • Performed source to target mapping as part of data migration from JD Edwards system to Agile PDM system.
  • Data Migration testing and implementation activities using SSIS and SSRS tools of Microsoft SQL Server 2008.
  • Responsible for accuracy of the data collected and stored in the corporate support system.
  • Performed data review, evaluate, design, implement and maintain company database.


Associate Software Engineer


  • Documented the technical specification for the reports and tested the generated reports.
  • Gathered user requirements and created the business requirements documents, used the technical document to design tables.
  • Prepared test plans for various modules.
  • Created and managed Databases and optimized the SQL queries for improved performance.
  • Created Database triggers to maintain the audit data in the tables and prepared test plans for various modules
  • Developed worksheets using parameters Blend, Join, calculate data to analyze the data set and visualize data in form of various chartsplots and maps.
  • Designed different Dashboards to analyze the data related to customers, sales and services by applying actionable insights.
  • Loan prediction data set using R
  • Customer details gender, marital status, income, loan amount, credit history is studied and percentage of Loan approval is predicted.
  • Loan status is set as target value and compared by other variables like loan status by income, loan status, Credit history and a model is developed to predict the target variable.
  • Decision tree model is used to base the predictions on the variables and the result of loan status.

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