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Big and Complex Data Analysis: Methodologies and Applications (Contributions to Statistics)

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This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field.

The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data.

The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data....

Computational Physics: Problem Solving with Python
Computational Physics: Problem Solving with Python
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.

This upper-division text provides an
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Big Data Visualization
Big Data Visualization
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book...

Scientific Computing with Python 3
Scientific Computing with Python 3

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Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics
Practical Data Wrangling: Expert techniques for transforming your raw data into a valuable source for analytics

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  • Work with different types of datasets, and reshape the layout of your data to make it easier for analysis
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Lean Python: Learn Just Enough Python to Build Useful Tools
Lean Python: Learn Just Enough Python to Build Useful Tools

Learn only the essential aspects of Python without cluttering up your mind with features you may never use. This compact book is not a "best way to write code" type of book; rather, the author goes over his most-used functions, which are all you need to know as a beginner and some way beyond.

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