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The University of Texas at Dallas Expected: December 2020
M.S., Business Analytics GPA 3.67
Osmania University July 2018
B.E., Electronics and Communication GPA 4.0
TECHNICAL SKILLS & COMPETENCIES:
Analytics & Data Science: Data Importing, wrangling, manipulation, querying, statistical modeling, and visualization.
Business Analytics and Visualization: R, Matlab, Alteryx, PowerBI, R Shiny, Tableau Desktop, Tableau Prep, Excel.
Databases: PostgreSQL, Microsoft SQL, MySQL, Oracle, Mongo (NoSQL).
Currently Learning: Machine Learning, Deep Learning, Python, and SAS.
Competencies: Teamwork, Sense of Ownership, Commercial Awareness, Communication, Problem Solving, Leadership, Result-Oriented, and Organizational Skills.
Traffic density based signal duration modulation
Project lead March 2017 - June 2018
• Led a team to design a system to predict traffic, in an agile environment.
• Created a model to predict traffic density by blending concepts of Image Processing, and Data Analytics using Matlab reaching computing efficiency of 97% more than the other existing university Matlab models.
• Published the work in the “International Journal of Current Engineering and Scientific Research”.
ACADEMIC PROJECTS: December 2018 - Present
Next Chipotle Store: Data Wrangling, and Visualizing (R)
Researched the current locations of chipotle stores using a dataset provided by Thinknum, analyzing the current locations and hypothesized four locations that could bring high profits on opening a store.
International Debt Statistics: Data Importing, and Manipulation (SQL)
Analyzed World Bank 1970-2015 debt data by running PostgreSQL queries to answer country-debt related questions and extract the statistics of the data.
NYC Taxi Fares Prediction with Random Forests: Data Visualizing, and Machine Learning (R)
Predicted the value of fares & tips, based on location, date and time using Random Forest method in R, on a sample of 49999 rows data set from 2013 NYC taxi trips data.
Walmart Sales Prediction: Data Wrangling, Manipulation, and Visualizing(R)
Analyzed weekly store sales data for 45 Walmart stores and predicted sales for each of the individual store using linear regression model based on previous weekly sales, markdown events, temperature, and unemployment rate.
Popular baby names from 1954-2018: Data Cleaning, Wrangling, and Visualizing (R Shiny)
Created a shiny app using popular baby names in New Zealand. (It is live at: ------------)
• Completed over 24 courses and 1,349 exercises earning over 104,597 XP in DataCamp.
• Ranked in the top 5 in over 240 students in a test in Statistics and Data Analytics having scored 99 of 102.
Eligibility: Eligible to work in the U.S. for internships and for full-time employment for up to 36 months without sponsorship.
R, Matlab, Alteryx, PowerBI, R Shiny, Tableau, wrangling, manipulation, querying, visualization