The Handbook of Data Science and AI
Generate Value from Data with Machine Learning and Data Analytics
1st Edition
Published on 30. November 2022
Book
Hardback
573 pages
978-1-56990-886-0 (ISBN)
Description
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
- BONUS in print edition: E-Book inside
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
More details
Other editions
New editions
Stefan Papp | Zoltan Toth | Katherine Munro
The Handbook of Data Science and AI
Generate Value from Data with Machine Learning and Data Analytics
Book
04/2026
3rd Edition
Hanser Publications
€99.99
Available immediately
Persons
Author
Stefan Papp is an entrepreneur who works with Fortune 500 companies to build data platforms and helps them to become more data-driven. Living with his family in Armenia, he is also involved in the Armenian startup ecosystem, and he acts there as an advisor and investor.
Wolfgang Weidinger is a Data Scientist and AI professional. He has worked in a wide variety of industries and sectors such as start-ups, finance, consulting, wholesale and insurance. There he led Data Science & AI teams and drove their role as spearheads in digital and data-driven transformation. He is President of the Vienna Data Science Group (www.vdsg.at), a non-profit association of and for Data Scientists and all other Data & AI professionals.
ISNI:
ISNI:
Katherine Munro is a Data Scientist, Data Science Ambassador and Computational Linguist, conducting research and development and corporate training in AI, Natural Language Processing and Data Science. Katherine began her tech career specializing in user interfaces and Natural Language Understanding, with roles at Mercedes-Benz and the Fraunhofer Institute. Currently she is building smart conversational AI systems using NLP techniques and Large Language Models.
Dr. Danko Nikolic is an expert in both brain research and AI. For many years he has run an electrophysiology lab at the Max-Planck Institute for Brain Research. Also, he is an AI and machine learning professional heading a Data Science team and developing commercial solutions based on AI technology.
Zoltan C. Toth is a data engineering architect, lecturer and entrepreneur. With a background in Computer Science and Mathematics, he has taught data architectures, big data technologies and machine learning operations to Fortune 500 companies worldwide. In the past two decades he has worked with several large enterprises as a Solutions Architect, implementing data analytics infrastructures and scaling them up to processing petabytes of data.