Anish Gamadia

Data Center Operations Technician: https://epic.avature.net/Careers/FolderDetail/Data-Center-Operations-Technician/25587

With hands-on experience using Epic and Athena EMR systems, combined with strong programming skills in R, Python, and SQL, I bring a unique blend of technical and healthcare knowledge to the Data Center Operations Technician role. My background as a scribe and intern leader in a clinical environment has honed my ability to manage complex workflows, troubleshoot issues efficiently, and collaborate with multidisciplinary teams. I am adept at optimizing data management processes, ensuring system reliability, and implementing solutions that enhance operational efficiency and data integrity. My diverse experience positions me well to support and maintain robust data center operations in a dynamic and high-stakes environment.

PCEP – Certified Entry-Level Python Programmer

Data Science Project on Heart Disease Prediction

Conducted a comprehensive data science project to analyze and visualize a heart disease dataset from four different sources: Cleveland, Hungary, Switzerland, and Long Beach V. The dataset, consisting of 76 attributes, was refined to a subset of 14 key features relevant to heart disease prediction. Employed data analysis and visualization techniques to uncover patterns and insights within the data. Built and evaluated machine learning models to predict the presence of heart disease, optimizing for accuracy and interpretability. The project demonstrated proficiency in data preprocessing, feature selection, model development, and the application of statistical and machine learning techniques to real-world healthcare data.

Data Science Project on Cancer Diagnosis Using CT Scans

Led a data science project focused on the analysis of CT scan images to diagnose cancer, leveraging advanced machine learning and deep learning techniques. Developed a robust pipeline for data preprocessing, including image normalization, augmentation, and feature extraction, to enhance the quality and usability of the data. Applied convolutional neural networks (CNNs) and other state-of-the-art models to accurately classify CT scans and predict the presence of cancer. Evaluated model performance using precision, recall, F1-score, and AUC metrics, and fine-tuned models for optimal diagnostic accuracy. The project showcased strong skills in medical imaging analysis, computer vision, and machine learning, contributing valuable insights for early cancer detection and treatment planning.