Data Science in Practice is the ideal introduction to data science. With or without math skills: Here you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be.
Data Science in Practice is the ideal introduction to data science. With or without math skills: Here you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be.
Tom Alby has been working in the digital world since 1994, including nearly 20 years for search engines such as Lycos, Ask.com and Google. His focus is on data-driven applications for everyday business and the development of data literacy. He is the author of several books, lecturer for Data Science and Digital Analytics at various universities and certified project manager (PMP) of the Project Management Institute since 2004.
Inhaltsangabe
1. Introduction 2. Machine Learning, Data Science and Artificial Intelligence 3. The Anatomy of a Data Science Project 4. Introduction to R 5. Exploratory Data Analysis 6. Forecasting 7. Clustering 8. Classification 9. Other use cases 10. Workflows and Tools 11. Ethical handling of data and algorithms 12. Next Steps after this book? 13. Appendix: Troubleshooting 14. Glossary
1. Introduction 2. Machine Learning, Data Science and Artificial Intelligence 3. The Anatomy of a Data Science Project 4. Introduction to R 5. Exploratory Data Analysis 6. Forecasting 7. Clustering 8. Classification 9. Other use cases 10. Workflows and Tools 11. Ethical handling of data and algorithms 12. Next Steps after this book? 13. Appendix: Troubleshooting 14. Glossary
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