Data Project

SANJNA SRIVATSA

Project, papers and code available on GitHub and personal portfolio Sanjna.strikingly.com ********@***.*** in.linkedin.com/in/sanjnasrivatsa 301-***-**** github.com/sanjnasrivatsa Education

Master of Science, Information Management – Data Analytics and Machine Learning GPA: 4.0 University of Maryland, School of Information Studies, College Park, MD. Expected May 2017 Bachelor of Engineering, Information Science GPA: 3.6 Visvesvaraya Institute of Technology, Bangalore, India. July 2015 Professional Experience

Graduate Assistant, Digital Curation & Innovation Centre, Maryland [January 2016 - current]

• Analytics lead for processes of extracting and analyzing descriptive text and quantitative financial data using Spark on AWS, statistically model geographic anomalies over time and decipher insights on gentrification and mapping modern racial tensions.

• Student developer for the virtual computing lab, a cloud platform running on AWS with React.js, Angular.js and Typescript on front-end. Demonstrated ability to grasp quickly by learning to scale the platform using Ansible and vagrant to automate machine creation for 35000 users from current 150 users.

Data Analytics Intern, Talklocal, Maryland [May 2016 – August 2016]

• Worked directly with CEO to create advanced geographic data visualizations using Tableau for corporate partnerships and del ivered interactive dashboards for the sales team demonstrating increased efficiency of 20% by reducing query time. Data Scientist for Business Intern, National Association of Software & Services Company [Dec 2014 – April 2015]

• Performed probabilistic model ing to characterize complex start-up profile attributes to predict compatibility with Venture Capitalists & Investment firms to achieve a success rate of 92%.

• Guided by the regional head to analyze operational and revenue models of accelerators and publish a report aiding decision on resource allocation of $1 million, by partners to the best accelerators affecting 600 startups collectively housed. Student Partner, Girls in Tech Regional Lead, Microsoft Corporation, India. [April 2014 – Sept 2015]

• Employed data driven strategy based on female psychology and persuasion studies to lead a team of 7 which effectively improved participation of gi rls from diverse backgrounds in schools and colleges by 200% in one year. Technical Skills

Analytics and Visualization: R, Python (Numpy, Pandas, Scikit, Bokeh), Spark, Tableau, Big Data - (Hadoop, Hive), Tensor Flow Database Technologies: MySQL, NoSQL

Machine learning: Classification Trees, Regression and Predictive model ling, Clustering, Feature Selection, SVM, Neural Networks, Gaussian Models, Random Forests, Boosting, Natural Language Processing, Deep Learning Research Papers & Academic Projects

Supervised research - ‘Business Intelligence to aid decision making for institutional investments in the cyber security sector’

• Employed Bayesian theory yielding a simple digestible market indicator after pivoted sentiment analysis (patent pending), topical fi ltration and entity extraction aiming at financial market trend forecast. Machine learning course project – ‘Trend detection in information flow in times of disaster’

• In association with the UN general principal, using advanced R, leaflet.js and Python, we evaluated trend in information flow during the Nepal April 2015 Earthquake and verified it against May 2015 geo-located data, collected using twitter API. Advanced Machine Learning research project (ongoing) –‘Scoring vulnerability in emails, based on CVE Mitre and NVD datasets’.

• This grant funded is in collaboration with the University of Hawaii and aims to measure threat levels in emails. I am using similarity matching in corpora of emails employing parsing algorithms, lexical analyzers and classification in Python and Bokeh. Advanced data analytics research project – ‘Predicting hotel ratings and revenue impact using TripAdvisor dataset’

• Used regression and predictive model ling to execute customer segmentation and future trend model ling for big data using Hadoop and MapReduce, and used R and Python for data wrangling and sentiment analysis of user comments. Data Analytics course project - ‘predictive modelling evaluating factors influencing uninsured population in the United States’

• Performed multiple regression tests and demonstrated effective root-cause analysis and trend prediction using R MLH Hackathon winning project – ‘Bot to help teenagers fight cyber bullying’

• Bui lt a bot using AIML to interact with cyber bullying victims, extract content from social media sites, analyze context and help guide victims to the right content and resources to help them fight cyber bullying. Achievements – Recipient of the MIM Alumni Scholarship Award for Academic excellence, Winner of MLH Hackathon.

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