Python For Data Analytics: A Systematic Literature Review Of Tools, Techniques, And Applications

Abstract

In the era of big data, the ability to collect, process, and analyze data efficiently has become a vital component for decision-making across various industries. Python, as a versatile programming language, has emerged as a powerful tool for data analytics due to its extensive libraries and user-friendly nature. This systematic literature review explores Python’s role in streamlining data analytics by examining its applications across various stages of the data analysis process, including data collection, cleaning, manipulation, and visualization. Key Python libraries such as NumPy, Pandas, and Matplotlib are discussed, highlighting their functionality in handling large datasets and enabling accurate and efficient analysis. Real-world examples demonstrate how Python can be applied in diverse sectors, from retail to healthcare, enhancing decision-making processes through data-driven insights. Furthermore, the limitations of Python, as well as alternative data analysis tools such as R and RapidMiner, are explored to provide a comprehensive view of Python’s place in modern data analytics. The review concludes that while Python offers significant advantages in data analysis, a combination of tools may often be necessary to meet the complex demands of today’s data-driven industries.