Data analytics for business : AI, ML, PBI, SQL, R - Garn, Wolfgang (Abingdon: Routledge, 2024)
"We are drowning in data but are starved for knowledge. Data Analytics for Business is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us to store data in a structured way. The Structure Query Language (SQL) allows us to gain first insights about business opportunities. Visualising the data using Business Intelligence tools and Data Science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models. For instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine Learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods. This can be used to define new market segments or group customers with similar characteristics. Finally, Artificial Intelligence allows us to reason under uncertainty and find optimal solutions for business challenges. All these topics are covered in this book with a hands-on process. That means, we use numerous examples to introduce the concepts and several software tools to assist us. Several interactive exercises support us in deepening the understanding and keep us engaged with the material. This book is appropriate for master students but can be used for undergraduate students. Practitioners benefit from the readily available tools. The material was especially designed for Business Analytics degrees with a focus on Data Science. It can also be used for Machine Learning or Artificial Intelligence classes. This entry-level book is ideally suited for a wide range of disciplines wishing to gain actionable data insights in a practical manner"--
Includes bibliographical references and index.
Keywords: Management -- Statistical methods; Management -- Data processing; Database management