Practical Research on Big Data Analysis and Statistical Inference Using Python

Abstract

The proliferation of data in modern industries has created a demand for statistical inference methods that are both predictive and scalable. This paper aims to close the gap between statistical inference theory and machine learning practice by utilizing Python’s rich functionality. This paper contributes and empirically demonstrates an end-to-end framework for a data scientist’s workflow, ranging from data cleaning and feature engineering to model construction and statistical validation. Through three real-world case studies in e-commerce, healthcare and finance, the paper empirically compares the relative merits of regularized regression, Bayesian classifiers, and ensemble methods. The findings reveal that Bayesian models offer superior uncertainty estimation in healthcare, where data is often scarce, whereas ensembles such as Gradient Boosting achieve state-of-the-art predictive accuracy in financial applications with big data. The paper emphasizes that statistical validation remains a mandatory step in building reliable machine learning systems. It also discusses practical challenges such as scalability, model interpretability, and data quality, and proposes mitigation solutions and future research directions. This research provides a practical guide to implementing statistical validation in data science workflows.