Confident Data Science - Discover the Essential Skills of Data Science
| Verlag | Kogan Page |
| Auflage | 2023 |
| Seiten | 408 |
| Format | 14,0 x 2,3 x 21,7 cm |
| Gewicht | 505 g |
| Artikeltyp | Englisches Buch |
| Reihe | Confident Series |
| EAN | 9781398612327 |
| Bestell-Nr | 39861232UA |
Discover the fundamentals of data science and develop the skills you need for achieving success in this important sector.
The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists.
With Confident Data Science, learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discipline, from primers on the key analytic and visualization tools to tips for pitching to and working with clients.
Adam Ross Nelson draws upon his expertise as a data science consultant and, as someone who made moved into the industry late in his career, to provide an overview of data science, including its key concepts, its history and the knowledge required to become a successful data scientist. Whether you are considering a career in this industry or simply looking to expand your knowledge, Confident Data Science is the essen tial guide to the world of data science.
About the Confident series...
From coding and data science to cloud and cyber security, the Confident books are perfect for building your technical knowledge and enhancing your professional career.
Inhaltsverzeichnis:
Section - SECTION ONE: Getting oriented;Chapter - 01: The untold history of data science;Chapter - 02: Genres and flavours of analysis;Chapter - 03: Data culture and the data science process;Section - SECTION TWO: Getting going;Chapter - 04: Data science examples in production;Chapter - 05: A weekend crash course;Section - SECTION THREE: Data science for clients;Chapter - 06: The client, the project and the data;Chapter - 07: Topic analysis;Chapter - 08: Regression;Chapter - 09: Classification;Chapter - 10: Sentiment analysis;Section - SECTION FOUR: Tools of the trade;Chapter - 11: Data sources;Chapter - 12: Data visualization;Chapter - 13: Python + R;Chapter - 14: Retrospective / prospective
Rezension:
"Value-packed on every page. Everything you need to break into data science is covered. Nelson also offers various technical examples through code to ensure every concept is thoroughly understood. I Highly recommend this book to both data science beginners and seasoned practitioners." Derrick Mwiti, Machine Learning Developer
