000			02790cam a22002418i 4500
001			23334627
005			20250630102734.0
008			230927s2024 enk b 001 0 eng
020			_a9781032372624
040			_aCSL _beng _cCSL
041			_2eng _aeng
084			_aB28:(X:74) R4 _qCSL
100	1		_aGarn, Wolfgang, _eauthor. _9752004
245	1	0	_aData analytics for business : _bAI, ML, PBI, SQL, R
260			_aAbingdon: _bRoutledge, _c2024.
300			_axii, 270p. _b: ill. _c; 25 cm.
504			_aIncludes bibliographical references and index.
520			_a"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"-- _cProvided by publisher.
650		0	_aManagement _xStatistical methods. _9814877
650		0	_aManagement _xData processing. _9814878
650		0	_aDatabase management. _9814879
942			_2CC _cTEXL _hB28:(X:74) R4 _n0
999			_c1433098 _d1433098