Data Science Handbook
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€59.99
Regular price
€65.99
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A01=Annalisa Cadonna
A01=Barbora Vesela
A01=Bernhard Ortner
A01=Danko Nikoli?
A01=Georg Langs
A01=Mario Meir-Huber
A01=Roxane Licandro
A01=Stefan Papp
A01=Wolfgang Weidinger
A01=Zoltan Toth
Age Group_Uncategorized
Age Group_Uncategorized
AI
Artificial Intelligence
Author_Annalisa Cadonna
Author_Barbora Vesela
Author_Bernhard Ortner
Author_Danko Nikoli?
Author_Georg Langs
Author_Mario Meir-Huber
Author_Roxane Licandro
Author_Stefan Papp
Author_Wolfgang Weidinger
Author_Zoltan Toth
automatic-update
Big Data
Business Intelligence
Category1=Non-Fiction
Category=GPH
Category=UYQM
COP=United States
Data Engineering
Data Scientist
Deep Learning
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_computing
eq_isMigrated=0
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Language_English
Machine Learning
PA=Available
Price_€50 to €100
PS=Active
softlaunch
Product details
- ISBN 9781569908860
- Weight: 862g
- Dimensions: 166 x 237mm
- Publication Date: 30 Nov 2022
- Publisher: Hanser Publications
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring:
- A comprehensive overview of the various fields of application of data science
- Case studies from practice to make the described concepts tangible
- Practical examples to help you carry out simple data analysis projects
The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term.
Contains these current issues:
- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.
- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice
- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies
- Computer vision: How can we gain insights from images and videos with data science?
- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.
- ML and AI in production: How to turn experimentation into a working data science product?
- Presenting your results: Essential presentation techniques for data scientists
Contributors: Stefan Papp / Wolfgang Weidinger / Katherine Munro / Bernhard Ortner / Annalisa Cadonna / Georg Langs / Roxane Licandro / Mario Meir-Huber / Danko Nikoli? / Zoltan Toth / Barbora Vesela / Rania Wazir / Günther Zauner
- A comprehensive overview of the various fields of application of data science
- Case studies from practice to make the described concepts tangible
- Practical examples to help you carry out simple data analysis projects
The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term.
Contains these current issues:
- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.
- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice
- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies
- Computer vision: How can we gain insights from images and videos with data science?
- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.
- ML and AI in production: How to turn experimentation into a working data science product?
- Presenting your results: Essential presentation techniques for data scientists
Contributors: Stefan Papp / Wolfgang Weidinger / Katherine Munro / Bernhard Ortner / Annalisa Cadonna / Georg Langs / Roxane Licandro / Mario Meir-Huber / Danko Nikoli? / Zoltan Toth / Barbora Vesela / Rania Wazir / Günther Zauner
The team of authors consists of data experts from business and academia. The spectrum ranges from executives to data engineers who create production systems, to data scientists who generate value from data. All authors are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and Machine Learning and raising awareness of the opportunities and potential risks of these technologies.
