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14.2 MB
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E56647022EFA4B4B5DC5012FD71310D14998C20A
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April 22, 2026, 4:07 p.m.
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(Last updated: April 22, 2026, 4:07 p.m.)
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| ['Sedkaoui S. Automated Data Analytics. Combining Human Creativity and AI...2024.pdf'] | 0 bytes |
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14.2 MB
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2026-04-22
| Uploaded by andryold1 | Size 14.2 MB | Health [ 18 /47 ] | Added 2026-04-22 |
NOTE
SOURCE: Sedkaoui S. Automated Data Analytics. Combining Human Creativity and AI...2024
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COVER
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MEDIAINFO
Textbook in PDF format Data analytics is a crucial process in today’s data-driven world. It involves collecting, cleaning, transforming and analyzing data to uncover useful information, insights, trends and patterns which inform business strategy, decision-making and process optimization. Traditionally, data analytics was a manual process requiring data scientists and analysts to prepare and process data before analyzing them. This was both tedious and time-consuming. The advent of machine learning and Artificial Intelligence (AI) has transformed data analytics by automating some parts of the process. Generative AI models, like ChatGPT, are at the forefront of this automation revolution in data analytics. These large language models can understand human prompts and generate coherent and human-like textual responses. They are trained with massive text datasets, enabling them to perform a variety of language-based tasks. Generative models, such as ChatGPT, can be fine-tuned for specific applications, including data analytics. After extensive training on a wide range of datasets and analytical tasks, these generative models evolve from toddlers into mature analytical assistants. They move from simple memorization of problem-solving techniques to the development of true conceptual understanding. The models understand why particular data transformations, models and evaluations are appropriate for given scenarios. The future of data analytics is humans and AI working together – combining the human’s imaginative definition of problems, ethics and strategic judgment with AI’s vast memory, exhaustive computation and high-velocity analytical workflows. Neither can match the synergistic value of the two intelligences combined. Data Science augments both human and Artificial Intelligence. Who this book is for: This book serves as an indispensable guide for the various stakeholders navigating the early stages of amplified analysis. For data scientists and analytics teams, it provides actionable frameworks for working responsibly with AI while retaining human creativity and human supervisions. The book’s insights will enable thoughtful development of analytical AI to unlock productivity while maintaining accountability. With illuminating case studies and prescriptive recommendations, data scientists can future-proof their skills and workflows to thrive in the age of automation. In academic institutions, this book offers an indispensable program both in Data Science programs and for interdisciplinary discussions. It enriches understanding of how best to develop AI-based techniques founded on moral principles and responsibility. The book sheds light on research avenues into reliable and transparent analytical algorithms. And it enables a nuanced discourse on the balance between productivity and prudence in the age of automation
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