I'm Aditya Sandeep Dalvi, a Software Developer currently living in Pune, India.
I graduated from Savitribai Phule University in 2021 with a degree in Mechanical Engineering. In the same year, I enrolled in a computer science course called
CS50 Introduction to Computer Science from Harvard Extension School.
Through this course, I learned C, data structures and algorithms, Python, SQL, HTML, CSS, JavaScript, and a bit of Flask. I quickly developed a passion for computer science, especially due to CS50's teaching approach. Consequently, in 2022, I undertook an intermediate-level course, CS50 Web Programming with Python and JavaScript. In this course, I learned how to develop scalable web applications using Python, JavaScript, and Django, which helped me in finding my first job at a startup called Myelin.
Engineered a dashboard used by over 100 clients, leveraging Python, Django, ChartJs, and JavaScript for real-time data visualization, improving user decision-making and operational efficiency.
Optimized API response time by 90% through optimizing database queries and refining the logic in REST APIs developed using Flask, Python, Node.js, and PostgreSQL.
Executed data cleaning and transformation using SQL, optimizing datasets by removing inconsistencies and handling missing values, resulting in enhanced accuracy and improved data analysis.
Developed an application for image annotation using Python, Django, and Plotly, allowing users to efficiently label and categorize images.
Engineered a web push notification application using Python and Django, enhancing user engagement.
Designed an automated bot using Python, Selenium, and OpenCV to download images and convert them to grayscale, streamlining image processing tasks.
Tested AI/ML models for object detection, evaluating their speed and accuracy to ensure optimal performance.
Architected backend models and REST APIs for an Online Exam Portal using Node.js, MongoDB, and ExpressJS, which streamlined test management and student evaluations. Facilitated seamless handling of over 1,000 exams and 5,000 student evaluations per month, improving processing efficiency by 75% and reducing manual errors by 40%.
Constructed backend models and APIs for applications tailored to schools, enhancing tracking of over 2,000 students' academic progress. Enabled teachers and parents to identify key traits, behaviors, and areas of improvement, resulting in a 30% improvement in student performance tracking and providing actionable insights that facilitated personalized education for 1,500 students.
