Prerana Chandrashekar

Prerana Chandrashekar has worked as a Data Analyst at TriZetto Provider Solutions since March 2021. In this role, they have designed and deployed ETL pipelines, developed SQL queries, and automated data pulls to enhance database efficiency and maintain data integrity.

Prior to that, they worked as a Data Analyst at IMG Systems from July 2020 to March 2021. Here, Prerana performed web scraping, maintained datasets, and built Tableau dashboards to visualize business KPIs and reduce initial cost.

From August 2019 to May 2020, Prerana served as a Graduate Teaching Assistant at the University of North Carolina at Charlotte. In this position, they provided academic support, tutored and mentored students, and assisted with projects and programming assignments.

Prerana also has experience as a Software Engineer at Accenture from October 2017 to July 2018. In this role, they monitored database productivity, improved query efficiency, and created informational reports for management based on SQL data.

Additionally, Prerana has worked as a Data Analytics professional at Pipegrep Technologies Pvt Ltd from April 2017 to September 2017. Here, they provided research expertise, gathered and synthesized data, and tested web applications for bugs and load time efficiency.

Prerana Chandrashekar's education history begins with a Bachelor of Engineering (BE) degree in Electronics and Communication Engineering, which they earned from BNM Institute Of Technology between 2013 and 2017. Following that, they pursued further education and completed a Master of Science (MS) degree in Computer Science from the University of North Carolina at Charlotte from 2018 to 2020.

In addition to their formal education, Prerana has obtained several certifications to enhance their skills. These include Tableau Essential Training from LinkedIn, obtained in May 2020, and various machine learning certifications from DataCamp, acquired in November and October 2019. The certifications from DataCamp cover topics such as machine learning, unsupervised learning, preprocessing for machine learning, supervised learning with scikit-learn, data cleaning, dealing with missing data, intermediate Python for data science, and introduction to data science in Python.