Data Science
Reference documents in Data Science.
Documents in Data Science
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This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.
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The future belongs to the companies and people that turn data into products. This book examines the many sides of data science — the technologies, the companies and the unique skill sets.
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The main goal of this book is to help people take the best actionable steps possible towards a career in data science. For Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees.
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This open-access textbook's significant contribution is the unified derivation of Data Assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem.
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This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. An essential resource for healthcare professionals and IT consultants.
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This book has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish.
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This book will be excellent for those that want to build a strong mathematical foundation for their knowledge on the main machine learning techniques, and at the same time get python recipes on how to perform the analyses for worked examples.
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Data Science is about drawing useful conclusions from large and diverse data sets through exploration, prediction, and inference. Our primary tools for exploration are visualizations and descriptive statistics, for prediction are machine learning and optimization, and for inference are statistical tests and models.
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The book covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are relevant to any industry and technology environment, and the learning is supported and explained with illustrative examples using open-source software.
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This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually.
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This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. This book is based on the Coursera Class "Developing Data Products" as part of the Data Science Specialization. Particular emphasis is paid to developing Shiny apps and interactive graphics.
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This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.
