PROGRAMMING FOR DATA SCIENCE: PYTHON, R, SQL, AND NOSQL Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.58532/nbennuraith3
· OA: W4411840307
Programming is the backbone of modern data science, enabling practitioners to manipulate, analyze, and extract insights from vast and complex datasets. This chapter explores the essential roles of Python, R, SQL, and NoSQL technologies in the data science workflow. Python, with its extensive libraries such as NumPy, Pandas, and Scikit-learn, has become the most widely used language for data manipulation, machine learning, and automation due to its simplicity and versatility [1, 2]. R remains a powerful tool for statistical analysis and visualization, especially in academia and research, offering specialized packages for modeling and graphics. SQL continues to be indispensable for querying and managing structured data in relational databases, while NoSQL solutions like MongoDB and Cassandra address the needs of unstructured and large-scale data. Mastery of these programming languages and tools allows data scientists to efficiently pre- process data, perform statistical analyses, build predictive models, and deploy solutions in real-world environments. As the field evolves, the integration of these technologies supports robust, scalable, and reproducible data science projects across diverse industries.
