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Data Scientist Entry level

Bloomington, IN
March 30, 2018

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**** *.*** ******, *** I Meghana Kantharaj +1-571-***-****

Bloomington IN 47408 EDUCATION

Indiana University, Bloomington, IN, United States May 2018 Master of Science in Data Science

PES Institute of Technology, Bengaluru, India May 2016 Bachelor of Engineering in Information Science


Data Mining and Data Warehousing, Machine Learning, Statistics, Exploratory Data Analysis, High Dimensional Data Analysis, Deep learning, Database Management Systems, Social Media Research, Bayesian Data Analysis, Software Engineering, Linear Algebra, Technology Entrepreneurship, Financial Engineering, Design Theory, Fuzzy Logic WORK EXPERIENCE

Indiana University, Bloomington, IN, USA February 2017 - Present Research Assistant

• Performed Scientometric analysis on papers published during 1901-2013 by observing the subject space as an ecosystem and studying the interactions between them.

• Data extracted and preprocessed using Beautiful Soup, UNIX Shell Programming, Python, Excel

• Data analyzed using Python and MySQL and visualized using Python SKILLS

Programming Languages: Python, R, C, SQL, PHP, HTML, XML, JavaScript Libraries: scikit-learn, numpy, scipy, matplotlib, pandas, dplyr, tidyr, ggplot, MySQLdb, readr Operating Systems: Microsoft Windows, Linux Ubuntu Tools: Weka, RStudio, XAMPP, Tableau, MS Excel

Databases: MYSQL

APIs: YouTube API, Twitter API


Studying Gender Stereotypes and Discrimination on YouTube and prediction of sexism in comments Fall 2017

• Studied patterns of most relevant search results on YouTube for various video categories and comments on the same targeting gender stereotypes and discrimination

• Built a classifier for YouTube comments as sexist/non-sexist independent of video contexts using Naïve Bayes NBA Player Rank Prediction Fall 2017

• Studied factors that influence performance of a player according to rankings from NBA season 2016

• Proposed a new, simpler metric to predict ranks and built a classifier using various ML algorithms to predict player performance

Natural Scene Classification: Spring 2016

• Developed a convoluted neural network classifier model of Deep learning architecture to categorize images

• Used Python and MIT Dataset for natural scenes of 256*256 jpegs for an efficiency of 59% for 4 classes Music Genre Recognition: Fall 2015

• Processed audio samples and classified them into their genres using Hidden Markov Models

• Signal processing techniques were used for format conversion and feature extraction during preprocessing

• Coded in python and used 1000 audio samples in 10 genres for an efficiency of 72% LEADERSHIP, HONORS AND AWARDS

• Recipient of scholarships for GHC ‘17, Strata Data Science ‘17 and Foundation for Excellence India 2012-2016

• Presented paper on Wine Quality Analysis using ML in National conference on Data Science and Analytics, India

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