机读格式显示(MARC)
- 000 03058nam a2200325 i 4500
- 008 230927s2024 enk b 001 0 eng
- 020 __ |a 9781032372624 |q (paperback)
- 020 __ |a 9781032372631 |q (hardback)
- 020 __ |z 9781003336099 |q (ebook)
- 040 __ |a DLC |b eng |e rda |c DLC
- 050 00 |a HD30.215 |b .G37 2024
- 082 00 |a 658.4/033 |2 23/eng/20230927
- 100 1_ |a Garn, Wolfgang, |e author.
- 245 10 |a Data analytics for business : |b AI, ML, PBI, SQL, R / |c Wolfgang Garn.
- 260 __ |a Abingdon, Oxon ; |a New York, NY : |b Routledge, |c 2024.
- 300 __ |a xii, 270 pages : |b illustrations ; |c 25 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references (pages 263-266) 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"-- |c Provided by publisher.
- 650 _0 |a Management |x Statistical methods.
- 650 _0 |a Management |x Data processing.
- 650 _0 |a Database management.