Data Analytics Information Systems

Meghana Vantipalli

Austin, TX *******************@*****.*** 512-***-****

EDUCATION

Texas State University, San Marcos, TX Aug 2022 – May 2024 Master of Science in Data Analytics & Information Systems GPA 4/4 Vasavi College of Engineering, Hyderabad, India Sep 2008 - May 2012 Bachelor of Engineering in Computer Science and Engineering CGPA 7.5/10 TECHNICAL PROFICIENCY

• Programming Languages: Python, SQL, R

• Database: MSSQL, MySQL, PostgreSQL

• Cloud Platform: AWS Redshift, S3, EC2, EMR, IAM, VPC, Azure Fabric

• Data Analytics & ML: PySpark, Jupyter notebook, Google Collab, Excel, SAP HANA BW, Simio, DBT, Rcommander, Data Mining, NLP, Text Mining, Deep Learning, Data modeling

• Visualization Tools: Power BI, Tableau

• Certifications: AWS Cloud Practitioner, HackerRank Python and SQL PROFESSIONAL EXPERIENCE

Adobe Commerce, Austin, TX Jan 2024 – May 2024

Graduate Intern

• Performed text mining on Adobe customer service support cases, explored large language models for clustering similar types of customer issues, utilized classification models and achieved 93% accuracy rate. Ushodaya Educational Institutions, Bodhan, India June 2012 - Aug 2016 Digital Marketing Analyst

• Managed digital ad campaigns, boosting student inquiries by 30% and increasing website traffic by 50% through SEO optimization.

• Optimized a 5,00,000 annual budget while promoting events, achieving a 20% rise in participation. HCL Technologies Ltd, Hyderabad, India May 2011-June 2011 Industrial Trainee

• Trained on Software Architecture, Software Models and Design Process ACADEMIC PROJECTS Aug 2022 – May 2024

Bank Customer Churn Analysis

• Utilized machine learning techniques to analyze bank customer churn, uncovering insights to optimize retention strategies.

• Tech Stack: RStudio, Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Sampling Methods.

EV vs Gas Car operational cost Analysis

• Analyzed historic electricity and gasoline prices using AR modeling to forecast prices for 3 years, conducting prescriptive analysis to recommend cost-effective vehicle types.

• Tech Stack: Pandas, matplotlib, Jupyter notebook, Python, AWS EC2, Decision Trees for Prescriptive Analytics.

Twitter Sentiment Analysis

• Analyzed restaurant Twitter reviews from Kaggle, cleaned data, utilized Naive Bayes for sentiment analysis, achieving an 80% polarity score with MulticlassClassificationEvaluator.

• Tech Stack: PySpark, AWS EMR, NLP Lib - StopWordsRemover, SnowballStemmer, Text Blob. Waiter Tip & House Price Prediction

• Utilized Linear Regression to forecast waiter tips based on restaurant type, bill amount, and other factors.

• Employed Linear Regression to predict house sale prices using factors like location, size, and room count.

• Tech Stack: PySpark, Pandas, Google Collab, EC2, matplotlib, ML Regression Evaluator, Jupyter notebook.

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