data scientist

Pragnakar Pedapenki [ Peter ]

Contact: 571-***-****: *********@*****.*** : www.linkedin.com/in/pragnakar Education

George Mason University, Fairfax, Virginia

MS in Data Analytics Engineering, December 2017

Coursework:

GITAM University, Visakhapatnam, India

B.Tech, Information Technology, August 2015

Technical skills

• Statistics and Probability: Modeling distribution, Hypotheses Testing, Regression Analysis.

• Visualization tools: Tableau, ggplot in R, matplotlib in Python.

• Software packages and tools: Gretl, various packages in Anaconda built on python like numpy, pandas, SciPY, Numba, scikit-learn, Keras, Tensorflow.

• Programming languages: Python, C++, R.

• Database: MySQL.

Certification

• edX Verified Certificate for Distributed Machine Learning with Apache Spark Work Experience

Document imaging specialist, Financial Aid Office: May 2016 - September 2016

• Handled confidential student financial aid application data and worked in teams. Intern(Trainee) at MissionRnD, IIIT- Hyderabad: June 2014 - July 2014

• Worked in teams to understand the concepts of computer networks and efferent software development. Projects [ Click to view on Github]

• Image classification of the STL-10 dataset using the convolutional neural network with Keras. [Summer 2017]

• Regression analysis to show correlation between birth rate and internet user percentage of country in R. [Spring 2016]

• Comparing the price of options of various companies with values estimated from Black–Scholes model and Monte Carlo simulation model. [ Fall 2016]

• Mutual fund ranking using Rank Reciprocal method to calculate weights and recommend mutual fund to invest in.

[Fall 2016]

• Classifying tumor whether it’s cancer or not using naïve Bayes classification. [spring 2017]

• Implemented K-nearest neighbor and collaborative filtering algorithms to predict movie ratings [Spring 2017].

• Implemented various mathematical models such as linear optimization, network optimization, sensitivity analysis, monte Carlo simulations and decision trees on real world example problems to achieve maximum efficiency in terms of cost and benefit. [ Spring 2016 ]

• Analytics & Decision Analysis

• Analytics: Big Data to Information

• Visualization for Analytics

• Financial Engineering

• Principles of Data Management and Mining

• Applied Predictive Analytics

• Decision and Risk Analysis

• Analytics for Finance and Econometrics

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