FUNDAMENTALS OF IMPLEMENTING DATA SCIENCE PROJECTS IN THE PYTHON PROGRAMMING LANGUAGE
Keywords:
Keywords: Data Science, API Data Retrieval, Requests , Data Collection, probability and statistics.Abstract
Abstract: Data Science has become a cornerstone of modern decision-making, enabling organizations to extract actionable insights from vast datasets. Python, with its rich ecosystem of libraries like NumPy, pandas, scikit-learn, and TensorFlow, is the de facto programming language for data science projects due to its versatility, readability, and extensive community support. Implementing data science projects in Python involves a systematic workflow encompassing data collection, preprocessing, modeling, evaluation, and deployment. However, challenges such as data quality, computational efficiency, and model interpretability often arise. This article explores the fundamentals of implementing data science projects in Python, addresses key challenges with practical solutions, and provides mathematical formulations and algorithms to support these methods
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