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Data analysis, Python, Excel, Statistics, R, SQL

Location:
San Carlos, CA
Posted:
January 04, 2019

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Resume:

Tanya Gulati ac74a8@r.postjobfree.com

San Carlos, CA, USA 415-***-****.

Summary

2+ years experience in

Data extraction, cleaning, merger, manipulation, exploration and visualization- exploring patterns and trends, statistical data analysis in SQL, Python and R

●Building, training and testing of supervised and unsupervised machine learning models applying regression, random forests, decision trees, neural networks, K Means clustering etc. algorithms in Python and R to deliver actionable insights from the data

●Have a good experience in Python more specifically writing code for machine learning applications

●Tools: Python ( scikit- learn, pandas), R, SPSS, Excel, SQL, Tableau

Education

Master of Science in Business Analytics (MSBA)

W. P. Carey School of Business at Arizona State University May 2018

PhD in Management (Quantitative Finance)

Birla Institute of Technology, India

Master of Business Administration

ICFAI Business School, India

Professional Experience

Research Assistant- Data Analytics (Current)

Arizona State University, Tempe, AZ June, 18-Current

Healthcare Industry Project

●Building machine learning models to predict outcome of a disease from real time data sets using neural networks

Performance Comparison of Machine Learning Platforms

●Built regression and classification models on 46 actual data sets using various machine learning algorithms such as random forest, decision tree, SVM, neural networks etc in R and Python

●Automated default and hyper parameter tuning in R and Python

Scholar

Birla Institute of Technology, Mesra, India Feb 09-April 17

Statistical Regression Models and Hypothesis Testing

●Performed data acquisition, merging and cleaning of data from different sources,, data exploration,discerning trends, presentation in charts and graphs and analysis

●Built statistical regression model to test whether investment bankers’ discretionary powers of allocation of IPOs contribute towards its price discovery.

● Built statistical regression model to test whether reduction in short selling restrictions leads to higher market efficiency and price discovery.

●Framed Hypothesis and tested it using descriptive and inferential statistics like A/B testing, Spearman rank correlation, cross tabulation, regression and independent t test.

Equity Analyst

B & K Securities, Mumbai, India April 06-Mar 07

●Recommending stocks to institutional investors for investment in oil & gas sector. Work involved building forecasting models and performing sensitivity analysis in excel.

Projects

Recommendation System

●4 month highly-collaborative project with the company “Find Your Influence” to build a recommendation system to match advertisers with influencers.

●Used SQL to retrieve data. Explored and visualized data in Tableau and excel using, VLookup, pivot tables and histograms

●Built logistic regression & K Means clustering models. Achieved 70 % accuracy in listing which ‘influencer’ should be recommended to the advertisers

Publications

●Gulati, T. (2011). Demand curves and partial incorporation of information in IPO pricing. Journal of Finance and Accountancy. 8, 140-158.

●Gulati,T., Bose, S.K., & Roy,S. (2017). Short selling restrictions in 2005-2009 in Indian market and underpricing of initial public offerings. Journal of Economics and Finance. 41, 116-135.



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