Data Insight Application: A Comprehensive Approach to Data Analytics
DOI:
https://doi.org/10.48001/978-81-966500-7-0-5Keywords:
Data Analytics, Data Collection, Preprocessing, Visualization, Machine Learning, Predictive Analytics, Prescriptive Analytics, Django FrameworkAbstract
This paper presents a comprehensive survey of data analytics, encompassing its definition, techniques, applications, challenges, and future trends. The usefulness of data analytics in gleaning insightful information from massive databases is first highlighted in the study. We then explore the foundations of data analytics, including its historical evolution, key concepts, and terminology. Different data analytics types (descriptive, diagnostic, predictive, and prescriptive) are presented alongside the corresponding techniques for data collection, pre-processing, analysis, and visualization. The diverse applications of data analytics across various domains (business intelligence, marketing, healthcare, finance, social media, and supply chain management) are showcased. Challenges inherent to data analytics (data quality, privacy, scalability, talent shortage) and ethical considerations (privacy, bias, transparency) are identified. Real world case studies illustrate successful implementations. Finally, the paper discusses future trends in data analytics (artificial intelligence, edge analytics, augmented analytics) and concludes with recommendations for further research and implications for businesses and society.
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