Data Science, Big Data, Artificial Intelligence and Generative AI 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:
- Understand crucial data science concepts, from statistics and mathematics to legal and ethical considerations.
- Learn how to build data platforms and deploy safe and robust data projects to production.
- Gain the vocabulary to communicate technical requirements and roadmaps to diverse business stakeholders.
- Dive into practical case studies that illustrate how knowledge generated from data is changing various industries over the long term.
The team of authors consists of data experts from business and academia, including data scientists, engineers, business leaders and legal experts. Their broad, deep guide to all aspects of working with data and AI includes:
- Machine Learning Fundamentals: Foundations of mathematics and statistics, plus common frameworks for applying ML in practice: from statistical ML to neural networks, Transformers and AutoML
- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, and put it to use in real world applications
- Foundation Models and Generative AI: Understand the strengths, challenges, and practical considerations for working with generative models for text, image, video, and other types of data
- Modeling and Simulation: Model the behavior of complex systems and do a What-If analysis covering different scenarios
- Data Science, ML and AI in production: How can you use cloud and database technologies and MLOps to turn experimentation into a working data product?
- Talking about Data: Communication and presentation skills for effective data teams and innovative business leaders
- Building Safe, Responsible AI: Best practices in ML Security; safeguarding generative AI models from attack; and how to adhere to GDPR, CCPA, and the new AI act
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.
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.
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.
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.
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.
Dr. Danko Nikolić 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.
