About

Meghana Chenreddy is an aspiring data scientist with a passion for using analytics to discover actionable insights. She holds a Bachelor's degree in Technology from IIIT Jabalpur and is currently pursuing her Masters in Business Analytics from the University of Maryland- Robert H. Smith School of Business. Meghana has advanced knowledge of programming in Python, R, and SQL, and a deep understanding of both supervised and unsupervised machine learning. She aspires to leverage her skills to transform data into actionable insights that improve organizations’ operational efficiency and quality of decision-making. During her bachelors, Meghana developed a Hierarchical Facility Allocation model for optimizing the run time of hierarchical facility allocation and location model for maternal healthcare in India using Artificial Bee Colony Algorithm. She also worked on a project under the guidance of Prof. Sunil Agarwal where she forecasted arrivals of Onions to markets of Mumbai using ARIMA and Seasonal-ARIMA models. Meghana has experience in data cleaning, exploratory data analysis, time series analysis and forecasting, statistics, classification, regression, clustering, ensemble methods, data visualization, and natural language processing. Apart from her academic achievements, Meghana is a trained Kuchipudi Dancer, having performed at various events and competitions from the age of 8. She has 0.35 years of relevant experience as a Graduate Assistant at the University of Maryland- College of Behavioral and Social Sciences and as a Summer Intern at Happy Financial Services. Meghana's technical skills include SQL, Python, Data Scientist, Research Scientist, NLP, ML, and MySQL. Her education history includes a Bachelor's degree in Technology from IIIT Jabalpur and a Master's degree in Business Analytics from the University of Maryland. Meghana is a highly motivated individual with a strong work ethic, excellent communication skills, and a passion for data science. She is seeking opportunities to apply her skills and knowledge in a challenging and dynamic environment.