Data analytics for business AI, ML, PBI, SQL, R

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 enca...

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Bibliographic Details
Main Author: Garn, Wolfgang, (Author)
Format: Electronic
Language: English
Published: Abingdon, Oxon ; New York, NY : Routledge, 2024.
Subjects:
Management > Statistical methods.
Management > Data processing.
Database management.
elektronické knihy
electronic books
ISBN: 9781003858713
1003858716
9781003336099
1003336094
9781003858768
1003858767
9781032372631
103237263X
9781032372624
1032372621
Physical Description: 1 online resource

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Table of contents

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024 7 |a 10.4324/9781003336099  |2 doi 
035 |a (OCoLC)1427943224 
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100 1 |a Garn, Wolfgang,  |e author. 
245 1 0 |a Data analytics for business  |h [electronic resource] :  |b AI, ML, PBI, SQL, R /  |c Wolfgang Garn. 
264 1 |a Abingdon, Oxon ;  |a New York, NY :  |b Routledge,  |c 2024. 
300 |a 1 online resource 
520 |a 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 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 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. 
588 |a OCLC-licensed vendor bibliographic record. 
650 0 |a Management  |x Statistical methods. 
650 0 |a Management  |x Data processing. 
650 0 |a Database management. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://www.taylorfrancis.com/books/9781003336099  |y Full text 

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