Designed and implemented a structured ETL pipeline to ingest, clean, and transform a heart disease diagnostic dataset. Implemented data imputation and handled outliers for key variables, including the number of major vessels coloured by fluoroscopy and thalassemia, to enhance data quality and reliability. Conducted exploratory data analysis (EDA) in Python to identify trends across 10+ risk factors, including age, gender, blood pressure, heart rate, chest pain type, cholesterol levels, exercise-induced angina, resting ECG results, blood sugar levels, uncovering correlations and patterns critical to heart disease risk. Built an interactive dashboard in Power BI for easy insights to stakeholders. Documenting all phases in detail has ensured clarity over the process, reproducibility, and scalability while also providing data-driven recommendations to support heart disease prevention strategies.

Completed a comprehensive virtual internship in Data Analytics, where I gained hands-on experience in analyzing and interpreting large datasets. My primary responsibilities included data cleaning, manipulation, and visualization using tools such as Excel, SQL and Python. I applied advanced statistical techniques to identify trends, patterns, and insights that informed business strategies.Throughout the internship, I worked on real-world projects that involved:Data Collection and Cleaning: Received raw data from the company and performed extensive cleaning and preprocessing to ensure accuracy, consistency, and readiness for analysis.Data Analysis: Applied various statistical methods, including regression analysis, hypothesis testing, and clustering, to uncover actionable insights.Data Visualization: Created clear and informative visualizations using Excel and other visualization tools to communicate findings to stakeholders.Collaboration: Worked closely with a team of interns and mentors to tackle complex analytical problems, providing solutions that directly contributed to business decisions.This experience not only enhanced my technical skills but also strengthened my ability to think critically, solve problems, and present data-driven insights effectively.

In my role as an English Content Developer at Testbook.com, I honed my analytical and content creation skills within an educational context, focusing on competitive exam preparation. My transition towards data analysis is fueled by my foundational experience in analyzing patterns, structuring complex information, and crafting insightful content. Below are the core responsibilities and achievements in this role, framed to highlight my analytical skills relevant to a career in data analysis:1. Analysis in Educational Content: Spearheaded the analysis of question patterns for various competitive exams such as SSC (CGL, CHSL, CPO, GD, MTS, Stenographer) and teaching exams (UGC NET, CTET, KVS, DSSSB), applying statistical techniques to identify trends and anomalies in exam content.2. Gained proficiency in using LMS portals, not just for content delivery but also for analyzing learner engagement and performance data, which informed the development of targeted educational strategies.3. Utilized analytics to underpin the creation of educational content, ensuring questions and solutions were aligned with the latest exam patterns and student learning needs.4. Applied analytical thinking to the breakdown of English grammar rules and concepts, transforming abstract information into clear, concise, and easily digestible content for learners of varying levels.4. Worked closely with educational experts and the digital platform team to ensure the accuracy and relevance of content, showcasing my ability to collaborate effectively across different domains.Despite having its roots in content production, this experience has given me a strong foundation in analytics, critical thinking, and the ability to simplify and make complicated subjects easily understood. I can't wait to use my abilities in a data analysis position so I can support strategic planning and data-driven decision-making.