python for data analysis 3e wes mckinney

Python for Data Analysis 3e - Wes McKinney

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples

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Python for Data Science For Dummies - John Paul Mueller, Luca Massaron

Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples. Get a firm background in the basics of Python coding for data analysisLearn about data science careers you can pursue with Python coding skillsIntegrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databasesPython careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

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Fast Python for Data Science - Tiago Antao

Fast Python for Data Science is a hands-on guide to writing Python code that can process more data, faster, and with less resources. It takes a holistic approach to Python performance, showing you how your code, libraries, and computing architecture interact and can be optimized together. Written for experienced practitioners, Fast Python for Data Science dives right into practical solutions for improving computation and storage efficiency. You''ll experiment with fun and interesting examples such as rewriting games in lower-level Cython and implementing a MapReduce framework from scratch. Finally, you''ll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working. About the technologyFast, accurate systems are vital for handling the huge datasets and complex analytical algorithms that are common in modern data science. Python programmers need to boost performance by writing faster pure-Python programs, optimizing the use of libraries, and utilizing modern multi-processor hardware; Fast Python for Data Science shows you how.

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Foundational Python for Data Science - Kennedy Behrman

Data science and machine learning—two of the world''s hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world''s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

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R for Data Analysis in easy steps - Mike McGrath

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends.R for Data Analysis in easy steps, 2nd edition has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. The book begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted. Next, it describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data.R for Data Analysis in easy steps, 2nd edition contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each ... Unknown localization key: "more"

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Statistics and Data Visualisation with Python - Jesus Rogel-Salazar

This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.

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Python Workout - Reuven Lerner

Python Workout presents 50 exercises designed to deepen the reader’s skill with Python. Readers will not only tackle exercises using built-in data structures, but also more advanced techniques, such as functional programming, object-oriented programming, iterators, and generators. With each engaging challenge, readers will practice a new skill and learn how to apply it to everyday coding tasks. Key Features 50 hands-on exercises and solutions Basic Python sequence types Python dictionaries and sets Functional programming in Python Creating your own classes Working with Python objects Generator functions Intended for readers with basic Python skills. About the technology Python is a versatile, elegant, general purpose programming language. Essential for data analysis, web development, artificial intelligence, games, desktop apps, and more, Python skills are a hot commodity. Reuven M. Lerner, an independent consultant for more than two decades, teaches Python, data science, and Git to companies around the world. His Better developers newsletter and blog are read by thousands of Python developers each week. Reuven has written a monthly column, “At the Forge,” for Linux Journal since 1996 and is a panellist on the weekly Freelancers Show podcast. Reuven lives with his wife and three children in Modi’in, Israel, and can be reached ... Unknown localization key: "more"

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Learn AI-Assisted Python Programming, Second Edition - Daniel Zingaro, Leo Porter

See how an AI assistant can bring your ideas to life immediately! Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way—with the help of AI. It''s perfect for beginners, or anyone who''s struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming, Second Edition you''ll learn how to: Write fun and useful Python applications—no programming experience required! Use the GitHub Copilot AI coding assistant to create Python programs Write prompts that tell Copilot exactly what to do Read Python code and understand what it does Test your programs to make sure they work the way you want them to Fix code with prompt engineering or human tweaks Apply Python creatively to help out on the job AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it ... Unknown localization key: "more"

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Python Workout, Second Edition - Reuven Lerner

Python is a versatile, elegant, general purpose programming language. Essential for data analysis, web development, artificial intelligence, games, desktop apps, and more, Python skills are a hot commodity.

Objev podobné jako Python Workout, Second Edition - Reuven Lerner

Statistics II For Dummies - Deborah J. Rumsey

Continue your statistics journey with this all-encompassing reference Completed Statistics through standard deviations, confidence intervals, and hypothesis testing? Then you’re ready for the next step: Statistics II. And there’s no better way to tackle this challenging subject than with Statistics II For Dummies! Get a brief overview of Statistics I in case you need to brush up on earlier topics, and then dive into a full explanation of all Statistic II concepts, including multiple regression, analysis of variance (ANOVA), Chi-square tests, nonparametric procedures, and analyzing large data sets. By the end of the book, you’ll know how to use all the statistics tools together to create a great story about your data. For each Statistics II technique in the book, you get an overview of when and why it’s used, how to know when you need it, step-by-step directions on how to do it, and tips and tricks for working through the solution. You also find:  What makes each technique distinct and what the results say How to apply techniques in real life An interpretation of the computer output for data analysis purposes Instructions for using Minitab to work through many of the calculations Practice with a lot of ... Unknown localization key: "more"

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Data Analysis with Python - Rituraj Dixit

An Absolute Beginner''s Guide to Learning Data Analysis Using Python, a Demanding Skill for TodayKey FeaturesHands-on learning experience of Python''s fundamentals.Covers various examples of how to code end-to-end data analysis with easy illustrations.An excellent starting point to begin your data analysis journey with Python programming.DescriptionIn an effort to provide content for beginners, the book Data Analysis with Python provides a concrete first step in learning data analysis. Written by a data professional with decades of experience, this book provides a solid foundation in data analysis and numerous data science processes. In doing so, readers become familiar with common Python libraries and straightforward scripting techniques.Python and many of its well-known data analysis libraries, such as Pandas, NumPy, and Matplotlib, are utilized throughout this book to carry out various operations typical of data analysis projects.Following an introduction to Python programming fundamentals, the book combines well-known numerical calculation and statistical libraries to demonstrate the fundamentals of programming, accompanied by many practical examples. This book provides a solid groundwork for data analysis by teaching Python programming as well as Python''s built-in data analysis capabilities.What you will learnLearn the fundamentals of core Python programming for data analysis.Master Python''s most demanding data analysis and visualization libraries, ... Unknown localization key: "more"

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Statistical Analysis with Python For Dummies - Joseph Schmuller

Wrangle stats as you learn how to graph, analyze, and interpret data with Python Statistical Analysis with Python For Dummies introduces you to the tool of choice for digging deep into data to inform business decisions. Even if you're new to coding, this book unlocks the magic of Python and shows you how to apply it to statistical analysis tasks. You'll learn to set up a coding environment and use Python's libraries and functions to mine data for correlations and test hypotheses. You'll also get a crash course in the concepts of probability, including graphing and explaining your results. Part coding book, part stats class, part business analyst guide, this book is ideal for anyone tasked with squeezing insight from data. Get clear explanations of the basics of statistics and data analysisLearn how to summarize and analyze data with Python, step by stepImprove business decisions with objective evidence and analysisExplore hypothesis testing, regression analysis, and prediction techniques This is the perfect introduction to Python for students, professionals, and the stat-curious.

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Linear Algebra for Data Science with Python - John M. Shea

Learn how to use Python and associated data-science libraries to work with and visualize vectors and matrices and their operations, as well import data to apply these techniques. Learn basics of performing vector and matrix operations by hand, how to use several different Python libraries for performing operations.

Objev podobné jako Linear Algebra for Data Science with Python - John M. Shea

Python All-in-One For Dummies - Alan Simpson, John C. Shovic

Everything you need to know to get into Python coding, with 7 books in one Python All-in-One For Dummies is your one-stop source for answers to all your Python questions. From creating apps to building complex web sites to sorting big data, Python provides a way to get the work done. This book is great as a starting point for those new to coding, and it also makes a perfect reference for experienced coders looking for more than the basics. Apply your Python skills to data analysis, learn to write AI-assisted code using GitHub CoPilot, and discover many more exciting uses for this top programming language. Get started coding in Python—even if you’re new to computer programmingReference all the essentials and the latest updates, so your code is air-tightLearn how Python can be a solution for large-scale projects and big datasetsAccelerate your career path with this comprehensive guide to learning PythonExperienced and would-be coders alike will love this easy-to-follow guide to learning and applying Python.

Objev podobné jako Python All-in-One For Dummies - Alan Simpson, John C. Shovic

Python for Scientific Computing and Artificial Intelligence - Stephen Lynch

Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/Solutions_Section_1.htmlSection 2: Python for Scientific Computing: ... Unknown localization key: "more"

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Ultimate Data Science Programming in Python - Saurabh Chandrakar

In today''s data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of data science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization. Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based data science.

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Python for Cybersecurity

Discover an up-to-date and authoritative exploration of Python cybersecurity strategies Python For Cybersecurity: Using Python for Cyber Offense and Defense delivers an intuitive and hands-on explanation of using Python for cybersecurity. It relies on the MITRE ATT&CK framework to structure its exploration of cyberattack techniques, attack defenses, and the key cybersecurity challenges facing network administrators and other stakeholders today. Offering downloadable sample code, the book is written to help you discover how to use Python in a wide variety of cybersecurity situations, including: Reconnaissance, resource development, initial access, and executionPersistence, privilege escalation, defense evasion, and credential accessDiscovery, lateral movement, collection, and command and controlExfiltration and impact Each chapter includes discussions of several techniques and sub-techniques that could be used to achieve an attacker's objectives in any of these use cases. The ideal resource for anyone with a professional or personal interest in cybersecurity, Python For Cybersecurity offers in-depth information about a wide variety of attacks and effective, Python-based defenses against them.

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Beginning Programming with Python For Dummies - John Paul Mueller

Create simple, easy programs in the popular Python language Beginning Programming with Python For Dummies is the trusted way to learn the foundations of programming using the Python programming language. Python is one of the top-ranked languages, and there’s no better way to get started in computer programming than this friendly guide. You’ll learn the basics of coding and the process of creating simple, fun programs right away. This updated edition features new chapters, including coverage of Google Colab, plus expanded information on functions and objects, and new examples and graphics that are relevant to today’s beginning coders. Dummies helps you discover the wealth of things you can achieve with Python. Employ an online coding environment to avoid installation woes and code anywhere, any time Learn the basics of programming using the popular Python language Create easy, fun projects to show off your new coding chops Fix errors in your code and use Python with external data sets Beginning Programming with Python For Dummies will get new programmers started—the easy way.Â

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Geographic Data Science with Python - Dani Arribas-Bel, Sergio Rey, Levi John Wolf

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

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Introduction to Python for Engineers and Scientists - Sandeep Nagar

Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you''ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You''ll Learn Understand the fundamentals of the Python programming language Apply Python to numerical computational programming projects in engineering and science Discover the Pythonic way of life Apply data types, operators, and arrays Carry out plotting for visualization Work with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.

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Applied Data Analysis for Urban Planning and Management

This book showcases the different ways in which contemporary forms of data analysis are being used in urban planning and management. It highlights the emerging possibilities that city-regional governance, technology and data have for better planning and urban management - and discusses how you can apply them to your research. Including perspectives from across the globe, it’s packed with examples of good practice and helps to demystify the process of using big and open data. Learn about different kinds of emergent data sources and how they are processed, visualised and presented. Understand how spatial analysis and GIS are used in city planning. See examples of how contemporary data analytics methods are being applied in a variety of contexts, such as ‘smart’ city management and megacities. Aimed at upper undergraduate and postgraduate students studying spatial analysis and planning, this timely text is the perfect companion to enable you to apply data analytics approaches in your research.

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Programming with Python for Engineers - Gokturk Ucoluk, Sinan Kalkan, Onur T. Sehitoglu

This book introduces computing and programming with undergraduate engineering students in mind. It uses Python (Version 3) as the programming language, chosen for its simplicity, readability, wide applicability and large collection of libraries. After introducing engineering-related Python libraries, such as NumPy, Pandas, Matplotlib, Sci-kit, Programming with Python for Engineers shows how Python can be used to implement methods common in a wide spectrum of engineering-related problems drawn from (for example): design, control, decision-making, scheduling and planning. Important features of the book include the following:   The book contains interactive content for illustration of important concepts, where the user can provide input and by clicking buttons, trace through the steps. Each chapter is also accessible as a Jupyter Notebook page and every code piece is executable. This allows the readers to run code examples in chapters immediately, to make changes and gain a better grasp of the concepts presented.     The coverage of topics is complemented by illustrative examples and exercises. For instructors adopting the textbook, a solutions manual is provided at https://sites.google.com/springernature.com/extramaterial/lecturer-material.

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Python For Kids For Dummies - Brendan Scott

The kid-friendly way to learning coding with Python Calling all wanna-be coders! Experts point to Python as one of the best languages to start with when you're learning coding, and Python For Kids For Dummies makes it easier than ever.

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Interactive Visual Data Analysis - Christian Tominski, Heidrun Schumann

In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data.The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis.The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book ... Unknown localization key: "more"

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Python for Kids, 2nd Edition - Jason R. Briggs

Python for Kids brings Python to life and brings kids (and their parents) into the wonderful world of programming. Author Jason R. Briggs guides readers through the basics, experimenting with unique (and often hilarious) example programs that feature ravenous monsters, secret agents, thieving ravens, and more. Full-colour illustrations keep things fun and engaging throughout! This second edition has been completely updated and revised to reflect the latest Python version and programming practices, with new puzzles to inspire readers to take their code farther than ever before.

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The Quick Python Book, Fourth Edition - Naomi Ceder

A fast-paced introduction to Python for intermediate developers–now with coverage of generative AI!For over 25 years, The Quick Python Book has been one of the best Python books money can buy. It concisely covers programming basics, while introducing Python''s comprehensive standard library and unique features in depth and detail. In this fourth edition, you''ll find new coverage of AI coding tools like Copilot and Google''s Colaboratory (Colab), and develop a mindset that can make the most of AI. The Quick Python Book, Fourth Edition includes: Python syntax, data structures, and best practices Python as an object oriented language Common Python libraries Basic data handling with Python Using AI code generation tools with Python Whether you''re new to Python or looking to advance your basic skills, The Quick Python Book, Fourth Edition will get you writing effective Python code fast. Python authority and former Chair of the Python Software Foundation Board or Directors Naomi Ceder has returned to author this extensively revised fourth edition. With the personal touch of a skilled teacher, Naomi beautifully balances details of the language with the insights and advice you need to handle any task. About the technology: System automation. High-performance web apps. Cloud and back-end ... Unknown localization key: "more"

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An Introduction to Categorical Data Analysis - Alan Agresti

A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the ... Unknown localization key: "more"

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Multivariate Data Analysis - Barry Babin, Joseph Hair, William ) Black, Rolph Anderson

For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today’s world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.

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Python in Excel Step-by-Step - David Langer

An intuitive guide for professionals wanting to prepare for the future of Microsoft Excel by building Python in Excel skills and unleashing the power of their data. A hands-on guide to the foundational Python in Excel skills you’ll need to understand and use this powerful analytics tool, Python in Excel Step-by-Step is for current Excel users interested in expanding their data analysis skillset with Python. Analytics educator and Microsoft Excel MVP David Langer demonstrates how to use Python in Excel, to unlock new analytics capabilities in Excel, and build your foundation for the future of Excel: do-it-yourself (DIY) data science. The book leverages your existing Excel knowledge to learn the Python foundation you can apply right away. This is the same approach David has used to successfully teach more than 1,000 professionals Python – even if you’ve never written code before. David also includes: Targeted coverage of the Python fundamentals required for analytics – learn just what you need fastHow to use the powerful pandas and plotnine libraries to facilitate data manipulation and visualization using Python in ExcelA DIY data science roadmap for you to build the skills you need to unleash the power of your data to have more ... Unknown localization key: "more"

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Learn Enough Python to Be Dangerous - Michael Hartl

All You Need to Know, and Nothing You Don''t, to Solve Real Problems with Python Python is one of the most popular programming languages in the world, used for everything from shell scripts to web development to data science. As a result, Python is a great language to learn, but you don''t need to learn "everything" to get started, just how to use it efficiently to solve real problems. In Learn Enough Python to Be Dangerous, renowned instructor Michael Hartl teaches the specific concepts, skills, and approaches you need to be professionally productive. Even if you''ve never programmed before, Hartl helps you quickly build technical sophistication and master the lore you need to succeed. Hartl introduces Python both as a general-purpose language and as a specialist tool for web development and data science, presenting focused examples and exercises that help you internalize what matters, without wasting time on details pros don''t care about. Soon, it''ll be like you were born knowing this stuff--and you''ll be suddenly, seriously dangerous. Learn enough about . . . Applying core Python concepts with the interactive interpreter and command line Writing object-oriented code with Python''s native objects Developing and publishing self-contained Python packages Using elegant, ... Unknown localization key: "more"

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Learn Python the Hard Way - Zed Shaw

You Will Learn Python! Zed Shaw has created the world''s most reliable system for learning Python. Follow it and you will succeed--just like the millions of beginners Zed has taught to date! You bring the discipline, persistence, and attention; the author supplies the masterful knowledge you need to succeed. In Learn Python the Hard Way, Fifth Edition, you''ll learn Python by working through 60 lovingly crafted exercises. Read them. Type in the code. Run it. Fix your mistakes. Repeat. As you do, you''ll learn how a computer works, how to solve problems, and how to enjoy programming . . . even when it''s driving you crazy. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Strings and text Interact with users Work with files Looping and logic Object-oriented programming Data structures using lists and dictionaries Modules, classes, and objects Python packaging Automated testing Basic SQL for Data Science Web scraping Fixing bad data (munging) The "Data" part of "Data Science" It''ll be frustrating at first. But if you keep trying, you''ll get it--and it''ll feel amazing! This course will reward you for every minute you put into it. Soon, you''ll know one of the ... Unknown localization key: "more"

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Mathematics for the IB Diploma: Analysis and approaches HL - Fannon Paul, Ben Woolley, Vesna Kadelburg, Stephen Ward, Huw Jones

Developed in cooperation with the International Baccalaureate®Enable students to construct, communicate and justify correct mathematical arguments with a range of activities and examples of maths in the real world.- Engage and excite students with examples and photos of maths in the real world, plus inquisitive starter activities to encourage their problem-solving skills- Build mathematical thinking with our new toolkit feature of questions, investigations and activities- Develop understanding with key concepts and applications integrated throughout, along with TOK links for every topic- Prepare your students for assessment with worked examples, and extended essay support- Check understanding with review exercise at the end of the coursebookFollows the new 2019 IB Guide for Mathematics: analysis and approaches Higher LevelAvailable in the seriesMathematics for the IB Diploma: Analysis and approaches SLStudent Book ISBN: 9781510462359Student Book Boost eBook ISBN: 9781398334304Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches SL 9781398321182Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches SL Boost eBook 9781398342316Mathematics for the IB Diploma: Analysis and approaches HLStudent Book ISBN: 9781510462366Student Book Boost eBook ISBN: 9781398334311Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches HL 9781398321878Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches HL ... Unknown localization key: "more"

Objev podobné jako Mathematics for the IB Diploma: Analysis and approaches HL - Fannon Paul, Ben Woolley, Vesna Kadelburg, Stephen Ward, Huw Jones

Mathematics for the IB Diploma: Analysis and approaches SL - Fannon Paul, Ben Woolley, Vesna Kadelburg, Stephen Ward, Huw Jones

Developed in cooperation with the International Baccalaureate®Enable students to construct, communicate and justify correct mathematical arguments, with a range of activities and examples of maths in the real world.- Engage and excite students with examples and photos of maths in the real world, plus inquisitive starter activities to encourage their problem-solving skills- Build mathematical thinking with our ''Toolkit'' and mathematical exploration chapter, along with our new toolkit feature of questions, investigations and activities- Develop understanding with key concepts and applications integrated throughout, along with TOK links for every topic- Prepare your students for assessment with worked examples, and extended essay support- Check understanding with review exercise midway and at the end of the coursebookFollows the new 2019 IB Guide for Mathematics: analysis and approaches Standard LevelAvailable in the seriesMathematics for the IB Diploma: Analysis and approaches SLStudent Book ISBN: 9781510462359Student Book Boost eBook ISBN: 9781398334304Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches SL 9781398321182Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches SL Boost eBook 9781398342316Mathematics for the IB Diploma: Analysis and approaches HLStudent Book ISBN: 9781510462366Student Book Boost eBook ISBN: 9781398334311Exam Practice Workbook Mathematics for the IB Diploma: Analysis and approaches HL 9781398321878Exam Practice ... Unknown localization key: "more"

Objev podobné jako Mathematics for the IB Diploma: Analysis and approaches SL - Fannon Paul, Ben Woolley, Vesna Kadelburg, Stephen Ward, Huw Jones

Classic Computer Science Problems in Python - David Kopec

Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means. Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you''ll remember important things you''ve forgotten and discover classic solutions to your "new" problems Key Features · Breadth-first and depth-first search algorithms · Constraints satisfaction problems · Common techniques for graphs · Adversarial Search · Neural networks and genetic algorithms · Written for data engineers and scientists with experience using Python. For readers comfortable with the basics of Python About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you’ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you’ll face as you grow your skill as a programmer. David Kopec teaches at Champlain College in Burlington, VT and is the ... Unknown localization key: "more"

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Qualitative Data Analysis - International Student Edition - Johnny Saldana, A. Michael Huberman, Matthew B. Miles

"This comprehensive, practical, user-friendly book provides a wealth of data analysis strategies that are essential for any qualitative research. It is a must-have tool book for moving from data analysis to writing for publication!"   –Guofang Li, University of British Columbia, Canada  Miles, Huberman, and Saldaña’s Qualitative Data Analysis: A Methods Sourcebook is the authoritative text for analyzing and displaying qualitative research data. The Fourth Edition maintains the analytic rigor of previous editions while showcasing a variety of new visual display models for qualitative inquiry. Graphics are added to the now-classic matrix and network illustrations of the original co-authors. Five chapters have been substantially revised, and the appendix’s annotated bibliography includes new titles in research methods. Graduate students and established scholars from all disciplines will find this resource an innovative compendium of ideas for the representation and presentation of qualitative data. As the authors demonstrate, when researchers "think display," their analyses of social life capture the complex and vivid processes of the people and institutions studied.

Objev podobné jako Qualitative Data Analysis - International Student Edition - Johnny Saldana, A. Michael Huberman, Matthew B. Miles

What Every Engineer Should Know About Python - Raymond J. Madachy

Engineers of all disciplines can benefit from knowledge of Python. This powerful programming language can help engineers better leverage their skill set and do more sophisticated work in a shorter time, including engineering analysis, machine learning, system design, integration and testing, and project management. What Every Engineer Should Know About Python provides engineering students and practitioners with a simple and practical introduction to Python for technical programming and other empowering uses for engineering and scientific work. It teaches the core features of Python relevant for engineers without computer science jargon, with the immediate goal of writing useful programs.• Features examples tied to real-world engineering and scientific scenarios that are easily adapted and incorporated• Covers how to best leverage the plethora of open source Python packages as opposed developing new software from scratch• Details the why, when, and how to incorporate Python into engineering designs and systems• Describes tool environments and software process best practices for rapid development• Demonstrates other uses of Python besides numerical computing, such as how to incorporate into engineering designs and systems, development tools and processes, and other ancillary uses to improve personal and organizational productivity through workflow automation• Points to more extensive material online for readers ... Unknown localization key: "more"

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An Introduction to Secondary Data Analysis with IBM SPSS Statistics - John MacInnes

Many professional, high-quality surveys collect data on people′s behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to: Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and ′clean′ secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you′ll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book′s companion website give you an opportunity to practice, check your understanding and work hands on with real data as you′re learning.

Objev podobné jako An Introduction to Secondary Data Analysis with IBM SPSS Statistics - John MacInnes

Qualitative Data Analysis - Pat Bazeley

Balancing theoretical foundations with practical strategies, this book helps you develop an approach to your qualitative analysis that is both systematic and insightful. It demonstrates the importance of tying analysis into every aspect of research, from design, through data collection and management, to writing up, and provides step-by-step guidance on how to embed analysis from start to finish. Grounded in the reality of doing research, this second edition:  • Presents visual and text-based methods for analysis, using manual and digital tools  • Inspires confidence as you code, connect and interrogate observational, text and visual data  • Showcases best practice and helps you navigate real-life dilemmas using case studies of research from across the social sciences. Together with rich online resources including videos, datasets and journal articles, this is an important new edition for all students undertaking qualitative research, with a focus on analysis and design.

Objev podobné jako Qualitative Data Analysis - Pat Bazeley

Hunting the Caliphate - Dana J. H. Pittard, Wes J. Bryant

In this vivid first-person narrative, a Special Operations Joint Terminal Attack Controller (JTAC) and his commanding general give fascinating and detailed accounts of America’s fight against one of the most barbaric insurgencies the world has ever seen. In the summer of 2014, three years after America’s full troop withdrawal from the Iraq War, President Barack Obama authorized a small task force to push back into Baghdad. Their mission: Protect the Iraqi capital and U.S. embassy from a rapidly emerging terrorist threat. A plague of brutality, that would come to be known as ISIS, had created a foothold in northwest Iraq and northeast Syria. It had declared itself a Caliphate—an independent nation-state administered by an extreme and cruel form of Islamic law—and was spreading like a newly evolved virus. Soon, a massive and devastating U.S. military response had unfolded. Hear the ground truth on the senior military and political interactions that shaped America’s war against ISIS, a war unprecedented in both its methodology and its application of modern military technology. Enter the world of the Strike Cell, secretive operations centers where America’s greatest enemies are hunted and killed day and night. Plunge into the realm of the Special Operations JTAC, American ... Unknown localization key: "more"

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Data Analysis in Qualitative Research - Iddo Tavory, Stefan Timmermans

From two experts in the field comes an accessible, how-to guide that will help researchers think more productively about the relation between theory and data at every stage of their work. In Data Analysis in Qualitative Research, Iddo Tavory and Stefan Timmermans provide a how-to guide filled with tricks of the trade for researchers who hope to take excellent qualitative data and transform it into powerful scholarship. In their previous book, Abductive Analysis: Theorizing Qualitative Research, Timmermans and Tavory offered a toolkit for innovative theorizing in the social sciences. In this companion, they go one step further to show how to uncover the surprising revelations that lie waiting in qualitative data—in sociology and beyond.  In this book, they lay out a series of tools designed to help both novice and expert scholars see and understand their data in surprising ways. Timmermans and Tavory show researchers how to “stack the deck” of qualitative research in favor of locating surprising findings that may lead to theoretical breakthroughs, whether by engaging with theory, discussing research strategies, or walking the reader through the process of coding data. From beginning to end of a research project, Data Analysis in Qualitative Research helps social scientists ... Unknown localization key: "more"

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Live Like Wes - Jessie Atkinson

What if Wes Anderson designed your house? His aesthetic is colourful, warm and inviting - perfect ingredients for beautiful home decor. Live Like Wes focuses on the interior design and styling of classic Wes Anderson films and shows you how to apply it at home. The book is split into eight chapters, each based on an Anderson movie and the aesthetic styling it best exemplifies - Bohemian kitsch for The Royal Tenenbaums, cottage-core for Fantastic Mr Fox, pattern for The Darjeeling Limited, European Charm for The French Dispatch, whimsical for Moonrise Kingdom, Mid-Century Modern for Asteroid City, symmetry for The Life Aquatic with Steve Zissou and dopamine decor for The Grand Budapest Hotel. So whether you''re in the market for a cottage-core kitchen straight out of Fantastic Mr Fox or you want to turn your dining room into a Mid-Century Modern masterpiece a la Asteroid City, there is something here no matter how deep you want to live your Wes fantasy.With well-researched film analysis and practical advice on how to get the look, the book includes stunning photos from 30 international interior designers who have emanated the Anderson aesthetic - to perfection. Each chapter also includes an illustrated step-by-step project ... Unknown localization key: "more"

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Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365) - Wayne Winston

Master business modeling and analysis techniques with Microsoft Excel and transform data into bottom-line results. Award-winning educator Wayne Winston''s hands-on, scenario-focused guide helps you use today''s Excel to ask the right questions and get accurate, actionable answers. More extensively updated than any previous edition, new coverage ranges from one-click data analysis to STOCKHISTORY, dynamic arrays to Power Query, and includes six new chapters. Practice with over 900 problems, many based on real challenges faced by working analysts. Solve real problems with Microsoft Excel—and build your competitive advantage Quickly transition from Excel basics to sophisticated analytics Use recent Power Query enhancements to connect, combine, and transform data sources more effectively Use the LAMBDA and LAMBDA helper functions to create Custom Functions without VBA Use New Data Types to import data including stock prices, weather, information on geographic areas, universities, movies, and music Build more sophisticated and compelling charts Use the new XLOOKUP function to revolutionize your lookup formulas Master new Dynamic Array formulas that allow you to sort and filter data with formulas and find all UNIQUE entries Illuminate insights from geographic and temporal data with 3D Maps Improve decision-making with probability, Bayes'' theorem, and Monte Carlo simulation and scenarios Use ... Unknown localization key: "more"

Objev podobné jako Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365) - Wayne Winston

An Introduction to Statistics and Data Analysis Using Stata® - Lisa Daniels, Nicholas W. Minot

The Second Edition of An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report provides an integrated approach to methods, statistics, data analysis, and interpretation of results. The book features examples from social science research and news articles along with concise descriptions of statistics, allowing readers to understand the context of data analysis while also learning to communicate the results.

Objev podobné jako An Introduction to Statistics and Data Analysis Using Stata® - Lisa Daniels, Nicholas W. Minot

Bioimage Data Analysis Workflows

This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.

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Python for Everyone, EMEA Edition - Cay S. Horstmann, Rance D. Necaise

Python for Everyone, 3rd Edition is an introduction to programming designed to serve a wide range of student interests and abilities, focused on the essentials, and on effective learning. It is suitable for a first course in programming for computer scientists, engineers, and students in other disciplines. This text requires no prior programming experience and only a modest amount of high school algebra. Objects are used where appropriate in early chapters and students start designing and implementing their own classes in Chapter 9. New to this edition are examples and exercises that focus on various aspects of data science.

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Practice of Computing Using Python, The, Global Edition - Richard Enbody, William Punch

For courses in Python Programming Now in its 3rd Edition, Practice of Computing Using Python continues to introduce both majors and non-majors taking CS1 courses to computational thinking using Python, with a strong emphasis on problem solving through computer science. The authors have chosen Python for its simplicity, powerful built-in data structures, advanced control constructs, and practicality. The text is built from the ground up for Python programming, rather than having been translated from Java or C++. Focusing on data manipulation and analysis as a theme, the text allows students to work on real problems using Internet-sourced or self-generated data sets that represent their own work and interests. The authors also emphasise program development and provide both majors and non-majors with a practical foundation in programming that will be useful in their respective fields. Among other changes, the 3rd Edition incorporates a switch to the Anaconda distribution, the SPYDER IDE, and a focus on debugging and GUIs.

Objev podobné jako Practice of Computing Using Python, The, Global Edition - Richard Enbody, William Punch

Applied Plant Science Experimental Design and Statistical Analysis Using SAS® OnDemand for Academics - Associate Professor Edward Durner

The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analysing and interpreting research can be overwhelming. SAS® is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic institutions.This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design and analysis of plant science experiments. It presents the most frequently used statistical methods in an easy-to-follow and non-intimidating fashion, and teaches the appropriate use of SAS® within the context of plant science research. This book:- Covers experimental designs and data analysis protocols- Is presented as a how-to guide with many examples- Includes freely downloadable data sets- Examines key topics such as merging data frames, multivariate analysis and linear regressionAuthored by an experienced teacher of applied plant science statistics, this book assumes no prior background in statistics and guides users through the appropriate methodologies in research. It is an invaluable tool for advanced undergraduate and graduate students, in addition to researchers, extension consultants, faculty and technicians.

Objev podobné jako Applied Plant Science Experimental Design and Statistical Analysis Using SAS® OnDemand for Academics - Associate Professor Edward Durner

Python for Excel Users - Tracy Stephens

Excel is a mainstay of the modern workplace, but it isn''t up to every task. Python for Excel Users offers a better way, showing how relatively simple programming can greatly boost productivity by streamlining workflows and adding new tools to the reader''s toolbox - no programming experience required. Uses include: Generating beautiful data visualizations, Creating interactive reports and dashboards, Tracking collaborative projects. This book leverages what readers already know about Excel to introduce useful coding applications. Practical examples illustrate how programming skills can be immediately applied to the reader''s day-to-day work.

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Artificial Intelligence Programming with Python - Perry Xiao

A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.

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Geographical Data Science and Spatial Data Analysis - Chris Brunsdon, Lex Comber

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges.Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Objev podobné jako Geographical Data Science and Spatial Data Analysis - Chris Brunsdon, Lex Comber