Название: Foundations of Data Science with Python
Автор: Jоhn М. Shеа
Издательство: CRC Press
Серия: The Python Series
Год: 2024
Страниц: 503
Язык: английский
Формат: pdf (true)
Размер: 37.9 MB

Foundations of Data Science with Python introduces readers to the fundamentals of Data Science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to Data Science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics. This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics.

Название: Statistics and Data Analysis for Engineers and Scientists
Автор: Таnvir Мustаfу, Мd. Таuhid Ur Rаhmаn
Издательство: Springer
Год: 2024
Страниц: 190
Язык: английский
Формат: pdf
Размер: 10.1 MB

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and Machine Learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.

Название: Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner
Автор: Dоthаng Тruоng
Издательство: CRC Press
Год: 2024
Страниц: 590
Язык: английский
Формат: pdf (true)
Размер: 35.9 MB

As data continues to grow exponentially, knowledge of Data Science and Machine Learning has become more crucial than ever. Machine Learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize Machine Learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. The book begins with Part I, introducing the core concepts of data science, data mining, and Machine Learning. My aim is to present these principles without overwhelming readers with complex math, empowering them to comprehend the underlying mechanisms of various algorithms and models. This foundational knowledge will enable readers to make informed choices when selecting the right tool for specific problems. In Part II, I focus on the most popular Machine Learning algorithms, including regression methods, decision trees, neural networks, ensemble modeling, principal component analysis, and cluster analysis.

Название: Geographic Data Science with Python
Автор: Sеrgiо Rеy, Dаni Аrribаs-Веl, Lеvi Jоhn Wоlf
Издательство: CRC Press
Год: 2023
Страниц: 411
Язык: английский
Формат: pdf (true)
Размер: 27.5 MB

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. 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.

Название: Introduction to Data Science
Автор: Gаоуаn Оu, Zhаnхing Zhu, Вin Dоng
Издательство: World Scientific Publishing
Год: 2024
Страниц: 445
Язык: английский
Формат: pdf (true)
Размер: 32.9 MB

Data Science is an emerging discipline which emphasizes the cultivation of Big Data talents with interdisciplinary ability. The book systematically introduces the basic contents of Data Science, including data preprocessing and basic methods of data analysis, handling special problems (e.g. text analysis), Deep Learning, and distributed systems. In addition to systematically introducing the basic content of Data Science from a theoretical point of view, the book also provides a large number of data analysis practice cases. Its purpose is to comprehensively introduce models and algorithms in Data Science from a technical point of view. This book systematically introduces the basic theoretical content of Data Science, including data preprocessing, basic methods of data analysis, processing of special problems (such as text analysis), Deep Learning, and distributed systems. In addition, this book provides a large number of case studies for data analysis application practice. Students can conduct practical training and interact with data on the iData-Course platform.

Название: Minimalist Data Wrangling with Python
Автор: Маrеk Gаgоlеwski
Издательство: Independently published
Год: 2024-01-25 (v1.0.3.9107)
Страниц: 436
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Minimalist Data Wrangling with Python is envisaged as a student's first introduction to data science, providing a high-level overview as well as discussing key concepts in detail. We explore methods for cleaning data gathered from different sources, transforming, selecting, and extracting features, performing exploratory data analysis and dimensionality reduction, identifying naturally occurring data clusters, modelling patterns in data, comparing data between groups, and reporting the results. Data Science aims at making sense of and generating predictions from data that have been collected in copious quantities from various sources, such as physical sensors, surveys, online forms, access logs, and (pseudo)random number generators, to name a few. They can take diverse forms, e.g., be given as vectors, matrices, or other tensors, graphs/networks, audio/video streams, or text. Data usually do not come in a tidy and tamed form. Data wrangling is the very broad process of appropriately curating raw information chunks and then exploring the underlying data structure so that they become analysable. This course uses the Python language which we shall introduce from scratch. Consequently, we do not require any prior programming experience. We will focus on developing transferable skills: most of what we learn here can be applied (using different syntax but the same kind of reasoning) in other environments. Thus, this is a course on data wrangling (with Python), not a course on Python (with examples in data wrangling).

Название: Статистика без подвоха: Методы критического анализа данных и причинного вывода
Автор: Итaн Бyэнo дe Mecкитa, Энтoни Фayлep
Издательство: ДМК Пресс
Год: 2023
Страниц: 455
Язык: русский
Формат: pdf
Размер: 17.4 MB

Введение в науку о данных или статистику не должно начинаться с доказательства сложных теорем или запоминания терминов и формул, но именно так устроены многие учебники по количественному анализу. В отличие от них эта книга посвящена критическому мышлению и концептуальному пониманию; она учит читателей быть вдумчивыми потребителями и аналитиками тех видов информации и аргументов, с которыми они будут сталкиваться на протяжении всей своей жизни. Книга, наполненная реальными примерами, показывает, как инструменты критического анализа применяются к проблемам в самых разных областях, включая выборы, гражданские конфликты, преступность, терроризм, финансовые кризисы, здравоохранение, спорт, музыку и космические путешествия. Прочитав эту книгу, вы узнаете, почему, несмотря на обширное применение данных в современном мире, они никогда не смогут заменить критическое мышление. В конце каждой главы есть упражнения, которые читатели могут выполнить самостоятельно, чтобы убедиться, что они усвоили материал. Некоторые из этих упражнений включают анализ данных, с ним могут справиться читатели и студенты, которые научились (или учатся) использовать статистическое рограммное обеспечение, такое как Stata или R.

Название: Privacy Preservation of Genomic and Medical Data
Автор: Аmit Кumаr Туаgi
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 559
Язык: английский
Формат: pdf (true)
Размер: 17.4 MB

Privacy Preservation of Genomic and Medical Data discusses topics concerning the privacy preservation of genomic data in the digital era, including data security, data standards, and privacy laws so that researchers in biomedical informatics, computer privacy and ELSI can assess the latest advances in privacy-preserving techniques for the protection of human genomic data. Privacy Preservation of Genomic and Medical Data focuses on genomic data sources, analytical tools, and the importance of privacy preservation. Topics discussed include tensor flow and Bio-Weka, privacy laws, HIPAA, and other emerging technologies like Internet of Things, IoT-based cloud environments, cloud computing, edge computing, and blockchain technology for smart applications. Data Science is a broad field encompassing some of the fastest-growing subjects in interdisciplinary statistics, mathematics and Computer Science. It encompasses a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, including diverse techniques under a variety of names, in different business, science, and social science domains. Similarly, data analytics is now required in the medial field for analyzing genomic and genetic data.

Название: Data Analytics: A Theoretical and Practical View from the EDISON Project
Автор: Juаn J. Сuаdrаdо-Gаllеgо, Yuri Dеmсhеnkо
Издательство: Springer
Год: 2023
Страниц: 486
Язык: английский
Формат: pdf
Размер: 10.1 MB

Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools. Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and Data Science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.

Название: Confident Data Science: Discover the Essential Skills of Data Science
Автор: Аdаm Rоss Nеlsоn
Издательство: Kogan Page
Серия: Confident Series
Год: 2023
Страниц: 409
Язык: английский
Формат: pdf (true), epub
Размер: 25.4 MB

The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists. With Confident Data Science, learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discipline, from primers on the key analytic and visualization tools to tips for pitching to and working with clients. Adam Ross Nelson draws upon his expertise as a data science consultant and, as someone who made moved into the industry late in his career, to provide an overview of Data Science, including its key concepts, its history and the knowledge required to become a successful data scientist. Whether you are considering a career in this industry or simply looking to expand your knowledge, Confident Data Scienceis the essential guide to the world of Data Science. In the first biggest family of Data Science techniques there is supervised machine learning. Supervised Machine Learning involves generating an algorithm that can make its predictions based on patterns the algorithm learned (figuratively speaking) from training data.