Pranav
Sankpal
Data Analyst · Data Scientist · Machine Learning Engineer
Data Analyst · Data Scientist · Machine Learning Engineer
Building a strong foundation in computer science, data science, and quantitative reasoning.
Linear Algebra, Calculus, Discrete Mathematics, Probability and Statistics, Data Structures, Algorithmic Thinking, and Software Engineering principles.
Professional development program focused on structured problem-solving, leadership, communication, adaptability, and workplace effectiveness through real-world business scenarios, collaborative learning, and practical skill development.
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Comprehensive data science curriculum covering statistics, Python for data analysis, machine learning, model evaluation, and real-world project work.
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Completed a virtual data science job simulation involving exploratory data analysis, preprocessing, feature engineering, and business insight generation using Python and real-world datasets.
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Learned SQL concepts, relational databases, querying techniques, and Oracle database fundamentals.
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HTML, CSS, JavaScript, responsive design, React, Node.js and full-stack development.
View Certificate ↗Applying machine learning and quantitative analysis to real-world financial market data.
Machine learning based stock prediction system using OHLCV market data, technical indicators, feature engineering, and predictive analytics. Built a complete pipeline from raw market data ingestion to model training, evaluation, and visualisation of predicted vs actual price movements.
An end-to-end machine learning project focused on customer churn prediction, behavioral analytics, feature engineering, exploratory data analysis, and predictive modeling using Python and Scikit-learn. The system analyzes demographic, transactional, service usage, and customer interaction data to identify churn drivers and generate actionable business insights for retention strategy.
Computer Science Engineering student with a strong passion for
data science, quantitative analysis, and algorithmic thinking.
Building a solid foundation in Python, statistics, data
structures, and machine learning — with a focus on data-driven
problem solving.
Developing skills in exploratory data analysis, visualisation, and
statistical reasoning — aiming to apply them in quantitative and
analytical roles at the intersection of data, mathematics, and
software.
Whether it's a data project, a collaboration, or just a conversation about data science and quantitative finance — my inbox is open.
