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Full Description
While data science is powering most modern organisations its technical intricacies often remain confined to a select few.
This book is for those starting to work within this field to give them the foundations on which to build their confidence and understanding. Allowing them to contribute to projects through making decisions and acting on them.
Written in an accessible manner, Data Science Foundations provides explanations of complex concepts and methods in layman's terms supported by examples. It also includes a holistic view of data science covering the technical, ethical and delivery challenges illustrated by case studies.
Aligned with industry-recognised qualifications, this is the definitive guide for aspiring data scientists, providing a solid foundation in data analysis techniques.
Contents
Chapter 1. Introduction
Chapter 2. Stakeholders
Chapter 3. Project Delivery
Chapter 4. Ethics and Lawfulness
Chapter 5. Discovery
Chapter 6. Properties of Data
Chapter 7. Sourcing
Chapter 8. Preparation
Chapter 9. Basic Concepts
Chapter 10. Model Selection
Chapter 11. Visualisations
Chapter 12. Model Evaluation
Chapter 13. Communication
Chapter 14. Machine Learning and Artificial Intelligence
Chapter 15. Case Studies
Chapter 16. Conclusion
