Focus : Football Analytics, Dynamic Team Formations, Real-time Optimization of Game Strategies, Predictive Modeling, High-value Internship Opportunities, Data-Driven Decision-making
Specifications : B.Sc Statistics (Honours), M.Sc Data Science
Skills
Python (Programming Language), Microsoft Excel, R (Programming Language), SQL, Front-End Development, Django, Java, Cloud Computing, Microsoft Power BI, Computer Vision,Languages
Bengali, English, Hindi,Hobbies
Watching and Analyzing Football Matches, Playing Chess, Calligraphy and Drawing
Summary
I’m Siddhartha Sinha, a data practitioner currently studying in Christ (Deemed to be University) with a statistics background from the University of Calcutta. I am currently working in advanced data analysis, computer vision, and multimodal integration, with projects including football analytics and AI-driven innovations. I am eager to leverage my skills to drive advancements in data science.
Education History
B.Sc Statistics (Honours) -
74 %
2019-07 /
2023-07
University of Calcutta
Internships
Data Science Intern -
Prodigy InfoTech
2024-03 /
2024-03
Data Science Intern -
CodSoft
2024-04 /
2024-05
Projects
Dynamic Team Formations in Football
2024-03 /
Present
Stellar Classification
- Optimizing football team performance using binary integer programming and network analysis for better tactical flexibility.
- Using computer vision and machine learning to predict gameplay and optimize player positioning for better tactics.
- Developing an AI-driven system for real-time formation adjustments, enhancing predictive capabilities and adaptability
2024-03 /
2024-05
UCL 2013-2014 Final Analysis
- Worked with a team to clean and analyze a 100,000-observation dataset, ensuring data integrity.
- Used statistical techniques (T-tests, ANOVA, regression) to analyze spectral characteristics of stars, galaxies, and quasars.
- Employed advanced ML methods, like ensemble techniques and neural networks, to improve classification accuracy.
2024-06 /
2024-07
Football Game Tracking Web Application
- Analyzed the 2014 UEFA Champions League Final to provide insights on team formations, player positioning, and key events.
- Utilized data visualization to assess player performance and identify trends for the two finalists’ post-match tactics.
- Produced a report with strategic recommendations, demonstrating expertise in football analytics and improving decisionmaking.
2024-08 /
Present
- Developing a football performance tracking app with Django, HTML, CSS, JavaScript, and Power BI for real-time analytics.
- Collaborating on a responsive frontend that integrates with backend APIs for efficient data processing.
- Building a scalable architecture with user authentication and personalized analytics, deploying it to the cloud with comprehensive testing.