 Название: Data Analytics for Business: AI-ML-PBI-SQL-R
Автор: Wolfgang Garn
Издательство: Routledge
Год: 2024
Страниц: 283
Язык: английский
Формат: pdf (true)
Размер: 29.2 MB
We are drowning in data but are starved for knowledge. Data Analytics 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 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, which can be used to define new market segments or group customers with similar characteristics. Finally, Artificial Intelligence (AI) 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, which 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 also be used for undergraduate students. Practitioners will also benefit from the readily available tools. The material was especially designed for Business Analytics degrees with a focus on Data Science and 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.
There are plenty of books about Data Analytics and Data Science available. This book differentiates itself by providing a practical introduction to databases and Business Intelligence tools. Furthermore, it includes a comprehensive chapter about Artifcial Intelligence. Of course, it covers the important area of Machine Learning.
Yet, the material goes into depth, which makes it interesting to a more technically inclined audience. Thus, it should be of interest to Data Science programmes and more generally to Computer Science courses. For instance, modules (classes) such as Machine Learning, AI and Databases can beneft from this text. This material is tailored in such a way that it can be taught within one semester.
A common understanding and overview of the theory, techniques and tools can be provided in the lectures. The laboratory sessions shall focus on solving business challenges and using software packages and coding languages. To be more specifc, the lab sessions introduce MySQL, Power BI, RStudio and R. Data Science languages such as R provide a platform to access the state-of-the-art methods for analysing business data. Data Analytics frameworks such as the Machine Learning roadmap give the essential steps in approaching typical Data Analytics projects.
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