Data analytics for business: AI-ML-PBI-SQL-R

By:

Garn, Wolfgang

Material type: Text

TextPublication details: Routledge London 2024Description: xii, 270 pISBN:

9781032372624

Subject(s):

DDC classification:

658.4033 GAR

Summary: 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. (https://www.routledge.com/Data-Analytics-for-Business-AI-ML-PBI-SQL-R/Garn/p/book/9781032372624?srsltid=AfmBOooSvRGXMRW9RR_tCJ8Z9krtQCwaysDk1-4OfUh2yexDMXcvMvAT)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	658.4033 GAR (Browse shelf(Opens below))	1	Available		007575

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Previous	658.4033 AND Quantitative methods for business	658.4033 DAE Management science: decision making through systems thinking	658.4033 FOX Mathematical modeling for business analytics	658.4033 GAR Data analytics for business: AI-ML-PBI-SQL-R	658.4033 GUP Quantitative techniques for decision making	658.4033 KAN Principles of mathematics in operations research	658.4033 KEL Statistics for management and economics	Next

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.

(https://www.routledge.com/Data-Analytics-for-Business-AI-ML-PBI-SQL-R/Garn/p/book/9781032372624?srsltid=AfmBOooSvRGXMRW9RR_tCJ8Z9krtQCwaysDk1-4OfUh2yexDMXcvMvAT)

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