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Data Science with less than a year in SQL, Python, Tableau, AWS, and Machine Learning.
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B.Tech graduate in Artificial Intelligence and Data Science with practical experience in SQL, Python, Tableau, AWS, and Machine Learning. Proficient in data analysis, statistical modeling, data visualization, and cloud analytics, with hands-on experience developing predictive models and serverless data pipelines. Passionate about transforming data into actionable insights and contributing to data-driven decision-making in business environments.
Marathwada Institute of Technology, Ch.Sambhajinagar
Bachelor of Technology · AI and Data Science
August 1, 2021 – June 30, 2025
Kulbhushan Junior College, Ch. Sambhajinagar
Maharashtra State Board XII
June 1, 2019 – May 31, 2021
Divvy Bike-Share Serverless Cloud Analytics Pipeline
June 16, 2026 – Present
Architected a serverless cloud data pipeline on AWS to analyze a massive, historical trip dataset for Divvy, a Chicago-based bike-share program with over 5,800 geotracked bicycles. Utilized Amazon S3 for storage and configured an AWS Glue Crawler to automate schema discovery and metadata registration in the Glue Data Catalog. Engineered a robust data transformation workflow in Amazon Athena using SQL. Executed complex CTAS (Create Table As Select) queries to combine multiple datasets into a unified full-year view, handle missing entries, and calculate performance-critical metrics such as ride length and day of week. Designed and published a high-performance interactive executive dashboard on Tableau Public. Translated analytical insights into actionable marketing recommendations such as targeted weekend promotions and seasonal membership campaigns to support conversion of casual riders into annual members.
View ProjectHeart Disease Prediction System
June 16, 2026 – Present
Developed an end-to-end Heart Disease Prediction System using 918 patient records, performing data cleaning, missing value treatment, outlier analysis, and comprehensive EDA to identify key health risk factors. Built and evaluated multiple machine learning models including Logistic Regression, Decision Tree, KNN, SVM, and Random Forest, achieving 89.1 test accuracy, 90.6 F1-score, and 93.0 ROC-AUC with a tuned Random Forest model. Implemented Scikit-learn Pipelines, Column Transformer, GridSearchCV, and 5-fold Cross-Validation, then deployed the solution through a Streamlit web application for real-time heart disease risk prediction.
Used Car Price Prediction
June 16, 2026 – Present
Developed a used car price prediction model using Linear Regression, Decision Tree, Random Forest, and ensemble techniques (Voting and Bagging) on 50,000+ records. Established Linear Regression as baseline model (R2 = 0.71) and improved model performance with Random Forest (R2 = 0.86) and ensemble methods, significantly enhancing prediction reliability. Conducted exploratory data analysis (EDA), handled missing data, and applied feature engineering to enhance model performance.
SQL for Data Analytics Certificate
Udemy
January 1, 2024 – Present
Python for Data Science Certificate
NPTEL (IIT Madras)
January 1, 2023 – Present
Google Data Analytics Professional Certificate
Coursera
January 1, 2022 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of applications within data science, from predictive modeling (car prices, heart disease) to cloud analytics pipelines (Divvy Bike-Share). This breadth of interest and application aligns well with a culture that values continuous learning and tackling varied challenges. The certifications further demonstrate a commitment to self-improvement and staying current with industry tools and practices. The projects also highlight an ability to work with different technologies (Python, SQL, AWS, Tableau), suggesting adaptability.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying data science concepts through academic projects and certifications. The project descriptions indicate an ability to work through complex data challenges, from raw data to actionable insights and deployment. While there is no direct professional experience, the project work suggests a capacity for problem-solving and structured thinking. The focus on end-to-end solutions (e.g., Heart Disease Prediction System) implies a good operational fit for roles requiring full lifecycle data science involvement.