Python Data Science Handbook

While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data.

This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several

Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targe

For college-level Computer Science courses in Python Basic Programming and Problem Solving in Python As one of the most widely used programming languages in the software industry, Python is desirable

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, di

This hands-on guide is aimed at practitioners of data science. The book assumes some previous skills with Julia and skills in coding in a scripting language such as Python or R, or a compiled language

Learn a simpler and more effective way to analyze data and predict outcomes with PythonMachine Learning in Python shows you how to successfully analyze data using only two core machine learning algori

This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics

“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has beco

Provides information on using the Python programming language to write automated data processing and visualization applications.

Computing Handbook, Third Edition: Computer Science and Software Engineering mirrors the modern taxonomy of computer science and software engineering as described by the Association for Computing Mach

As we move through the twenty-first century, the importance of science fiction to the study of English Literature is becoming increasingly apparent. The Science Fiction Handbook provides a comprehensi

As we move through the twenty-first century, the importance of science fiction to the study of English Literature is becoming increasingly apparent. The Science Fiction Handbook provides a comprehensi

The first edition of the Drilling Data Handbook was printed in 1950. In more than six decades, the book has been improved, adding lots of new technologies and equipment in its eight additional edition

Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and sh

Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-rang

This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge

Data-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative scie

Introduction to Computer Science Using Python: A Computational Problem-Solving Focus introduces readers to programming and computational problem-solving via a back-to-basics, step-by-step, objects-lat

Science is increasingly driven by data, and spatial data underpin the science directions laid out in the 2007 U.S. Geological Survey (USGS) Science Strategy. A robust framework of spatial data, metada

The book contains discussion on raster and vector data processing using Python binding of GDAL/OGR library. Also, different approaches of representing spatial reference system are enumerated. There is

A large amount of experimental data has been published since the debut of the original CRC Handbook of Thermodynamic Data of Aqueous Polymer Solutions. Incorporating new and updated material, the CRC

Intended as a reference for IT consultants and data analysts, this guide to data mining serves as a practical handbook for designing and implementing systematic programs for data culling and the produ

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal d

Written by a team of leading researchers in the field, this handbook presents a comprehensive overview of the state of the art in the theory and applications of missing data analysis. It covers histor

The Science Fiction Handbook offers a comprehensive and accessible survey of one of the literary world's most fascinating genres. Includes separate historical surveys of key subgenres including time-

The Science Fiction Handbook offers a comprehensive and accessible survey of one of the literary world's most fascinating genres. Includes separate historical surveys of key subgenres including time-

Provides information on using the Python programming language to write automated data processing and visualization applications.

The Handbook of Data Structures and Applications responds to the needs of students, professionals and researchers who need a mainstream reference on data structures by providing a comprehensive survey

The Handbook of Applied System Science is organized around both methodological approaches in systems science, and the substantive topic to which these approaches have been applied. The volume begins w

This complete update of a classic handbook originally created by Analog Devices and never previously published offers the most complete and up-to-date reference available on data conversion, from the

Data Analysis and Research for Sport and Exercise Science is tailored to suit undergraduate sports and exercise science students seeking a clear understanding of data and statistics to support their s

The International Handbook of Science Education is a two volume edition consisting of 77 chapters arranged into 10 sections pertaining to the most significant issues in science education. Current rese

Over the past thirty-five years, a tremendous body of both theoretical and empirical research has been established on the `science of transportation'. The Handbook of Transportation Science has col

With contributions by 42 of the best political scientists worldwide, A New Handbook of Political Science analyzes the progress made in political science over the past twenty years in the context of hi

This book is a customized version in Taiwan, and the content is extracted from Chapters 1 to 15 of the book "Introduction to Python Programming and Data Structures, Global Edition, 3rd edition".-- --For introductory courses in Python Programming.A fundamentals first approach to programming helps students create efficient, elegant code.Introduction to Python Programming and Data Structures introduces students to basic programming concepts using a fundamentals-first approach that prepares students to learn object-oriented programming and advanced Python programming. This approach presents programming concepts and techniques that include control statements, loops, functions, and arrays before designing custom classes. Students learn basic logic and programming concepts prior to moving into object-oriented and GUI programming. The content incorporates a wide variety of problems with various levels of difficulty and covers many application areas to engage and motivate students.For a list of

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure.Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.