Raghu Ram Batreddi

Organized and motivated student eager to apply time management and organizational skills in various environments. Seeking entry-level opportunities to expand skills while facilitating company growth.

• Create and Design Digital Products using Canva
• Data Communication and Network Services
• Statistical Analysis with R for Public Health Specialization, Coursera
• Data Visualization and Communication with Tableau, Coursera

Lead Ambassador, Center for Leadership and Service, University of North Texas, 10/2023, Present, I organize leadership workshops at UNT to help peers develop their communication, team-building, and leadership skills. As a liaison, I deliver presentations and provide consultations to promote the Center's mission. I collaborate strategically, execute programs innovatively, and advocate for continuous leadership engagement, significantly enhancing the campus's leadership culture.

COVID-19 Vaccine Progress Dashboard: Data Analysis by Zip Code

• Utilized Python libraries such as Pandas for data analysis and Tableau for data visualization to create insightful dashboards.
• Identified disparities in vaccination coverage across various demographics and geographic areas to help local health officials target outreach efforts in low-vaccination areas and promote more equitable vaccine distribution
• Developed skills in data analysis, data visualization, public health informatics, and team leadership through the project

Flight Delay Prediction and Analysis

• Led team project analyzing New York City flight data, consisting of 32,735 records, employing Python, Pandas, and Scikit-Learn for in-depth analysis.
• Implemented a Logistic Regression model, achieving an accuracy rate of 82%, while exploring flight delay patterns and factors, providing valuable insights to the aviation industry

Analysis and Visualization of US Election Data

• Utilized data cleaning scripts and Tableau to generate informative visualizations, including state rankings, average votes per election stage, and percentage of votes per political party, resulting in the creation of a detailed official report and dashboard for informed electoral strategies.

Sentiment Analysis with Deep Learning

• I used neural networks (RNNs or CNNs) for sentiment analysis on text data. Preprocessed and tokenized data, used word embeddings for semantic representation, and fine-tuned hyperparameters. Evaluated model performance using metrics like accuracy, precision, recall, and F1-score.