Описание: This book presents an introductory overview of spatial data analysis methods and geoinformation analysis techniques. Each chapter introduces the related theory, explains how to interpret metrics outputs, and provides worked examples using ArcGIS and GeoDa. This is a valuable resource for graduate students and researchers analyzing geospatial data.
Описание: This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.
Jump-start your career as a data scientist--learn to develop datasets for exploration, analysis, and machine learning
SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls.
You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data.
This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset."
Gain an understanding of relational database structure, query design, and SQL syntax
Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms
Review strategies and approaches so you can design analytical datasets
Practice your techniques with the provided database and SQL code
In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward
Автор: Mark Andrews Название: Doing Data Science in R: An Introduction for Social Scientists ISBN: 1526486768 ISBN-13(EAN): 9781526486769 Издательство: Sage Publications Рейтинг: Цена: 27720.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This approachable introduction to doing data science in R provides step-by-step advice on using data science 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.
Автор: Mark Andrews Название: Doing Data Science in R: An Introduction for Social Scientists ISBN: 1526486776 ISBN-13(EAN): 9781526486776 Издательство: Sage Publications Рейтинг: Цена: 9027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This approachable introduction to doing data science in R provides step-by-step advice on using data science 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.
Описание: This book provides a detailed description of the entire study process concerning gathering and analysing big data and making observations to develop a crime-prediction model that utilises its findings.
Автор: Subasi, Abdulhamit Название: Practical Machine Learning For Data Analysis Using Python ISBN: 0128213795 ISBN-13(EAN): 9780128213797 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.
Автор: Brooker, Phillip Название: Programming with python for social scientists ISBN: 1526431726 ISBN-13(EAN): 9781526431721 Издательство: Sage Publications Рейтинг: Цена: 8394.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge.
Автор: Brooker, Phillip Название: Programming with python for social scientists ISBN: 1526431718 ISBN-13(EAN): 9781526431714 Издательство: Sage Publications Рейтинг: Цена: 23285.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge.
Автор: Decaria Alex, Petty Grant W. Название: Python Programming and Visualization for Scientists ISBN: 0972903356 ISBN-13(EAN): 9780972903356 Издательство: Неизвестно Рейтинг: Цена: 10483.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Python continues to gain dominance as a language of choice for analyzing and visualizing scientific data. Although it has concise, intuitive syntax, learning how to plot and visualize data requires scouring the internet for documentation and examples. This book was written from the perspective of "What book would the authors want to have had when they were transitioning to Python?"
A second edition of the book was made necessary by the transition to Python 3, which did not maintain full backward compatibility with earlier versions of the language. The second edition has been completely revised to ensure that all code examples work in Python 3. Additional chapters on the Pandas library and Cartopy have been included, as well as an appendix on Jupyter notebooks, which have become an important tool for developing and communicating code in both the research and educational settings.
The 90 figures are mostly in color, and color syntax highlighting is used with all code samples throughout the text to facilitate visual recognition of program structure.
The first edition of the book has proven useful not only as a classroom text but also as a guide and reference for students, educators, and researchers who already have programming experience and want to start creating plots and analyzing data using Python. The second edition will serve the same role. It is not meant for the person who is completely new to programming, nor is it an introductory computer science textbook. The authors' assumptions are that the reader has some experience programming with a language other than Python.
Although the new Python programmer may wish to read the book cover to cover, the book is organized such that an experienced programmer can readily jump to the appropriate chapter. An extensive index aids in searching for functions and methods useful for data visualization and analysis.
Автор: Titscher S et al Название: Methods of Text and Discourse Analysis ISBN: 0761964835 ISBN-13(EAN): 9780761964834 Издательство: Sage Publications Рейтинг: Цена: 10294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An overview of linguistic and sociological approaches to text and discourse analysis.
Описание: Discusses the issues that the agricultural industry currently faces and the technological opportunities that can be explored to help protect and predict crop growth and achieve more resilient agricultural processes. The book analyses the impact of agricultural pollution and food insecurity and proposes solutions to promote sustainability.
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