The Data Science Framework
DOWNLOAD
Download The Data Science Framework PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The Data Science Framework book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
The Data Science Framework
DOWNLOAD
Author : Juan J. Cuadrado-Gallego
language : en
Publisher: Springer Nature
Release Date : 2020-10-01
The Data Science Framework written by Juan J. Cuadrado-Gallego and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-01 with Computers categories.
This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
Data Science And Big Data Computing
DOWNLOAD
Author : Zaigham Mahmood
language : en
Publisher: Springer
Release Date : 2016-07-05
Data Science And Big Data Computing written by Zaigham Mahmood and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-05 with Business & Economics categories.
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.
The Essentials Of Data Science Knowledge Discovery Using R
DOWNLOAD
Author : Graham J. Williams
language : en
Publisher: CRC Press
Release Date : 2017-07-28
The Essentials Of Data Science Knowledge Discovery Using R written by Graham J. Williams and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Business & Economics categories.
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
Data Analytics
DOWNLOAD
Author : Juan J. Cuadrado-Gallego
language : en
Publisher: Springer Nature
Release Date : 2023-11-10
Data Analytics written by Juan J. Cuadrado-Gallego and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-10 with Computers categories.
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. The following chapters have contributions by: Chapter 4, "Anomaly Detection" - Juan J. Cuadrado-Gallego, Yuri Demchenko, Josefa Gómez, and Abdelhamid Tayebi Chapter 5, "Unsupervised Classification" - Juan J. Cuadrado-Gallego, Yuri Demchenko, and Abdelhamid Tayebi Chapter 6, "Supervised Classification" - Juan J. Cuadrado-Gallego, Yuri Demchenko, and Josefa Gómez
Data Science
DOWNLOAD
Author : Zacharias Voulgaris
language : en
Publisher:
Release Date : 2017-08-05
Data Science written by Zacharias Voulgaris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-05 with categories.
Master the concepts and strategies underlying success and progress in data science. From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientist's toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework. The following chapters cover these four foundational areas: Chapter 1 - What Is Data Science? Chapter 2 - The Data Science Pipeline Chapter 3 - Data Science Methodologies Chapter 4 - The Data Scientist's Toolbox Chapter 5 - Questions to Ask and the Hypotheses They Are Based On Chapter 6 - Data Science Experiments and Evaluation of Their Results Chapter 7 - Sensitivity Analysis of Experiment Conclusions Chapter 8 - Programming Bugs Chapter 9 - Mistakes Through the Data Science Process Chapter 10 - Dealing with Bugs and Mistakes Effectively and Efficiently Chapter 11 - The Role of Heuristics in Data Science Chapter 12 - The Role of AI in Data Science Chapter 13 - Data Science Ethics Chapter 14 - Future Trends and How to Remain Relevant Targeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.
Data Science
DOWNLOAD
Author : Vijay Kotu
language : en
Publisher: Morgan Kaufmann
Release Date : 2018-11-27
Data Science written by Vijay Kotu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You'll be able to: - Gain the necessary knowledge of different data science techniques to extract value from data. - Master the concepts and inner workings of 30 commonly used powerful data science algorithms. - Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... - Contains fully updated content on data science, including tactics on how to mine business data for information - Presents simple explanations for over twenty powerful data science techniques - Enables the practical use of data science algorithms without the need for programming - Demonstrates processes with practical use cases - Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language - Describes the commonly used setup options for the open source tool RapidMiner
Building An Effective Data Science Practice
DOWNLOAD
Author : Vineet Raina
language : en
Publisher: Apress
Release Date : 2021-12-09
Building An Effective Data Science Practice written by Vineet Raina and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-09 with Computers categories.
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You’ll start by delving into the fundamentals of data science – classes of data science problems, data science techniques and their applications – and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. What You’ll Learn Transform business objectives into concrete problems that can be solved using data science Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project Build and operate an effective interdisciplinary data science team within an organization Evaluating the progress of the team towards the business RoI Understand the important regulatory aspects that are applicable to a data science practice Who This Book Is For Technology leaders, data scientists, and project managers
Science Framework For The 2011 National Assessment Of Educational Progress
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2011
Science Framework For The 2011 National Assessment Of Educational Progress written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Science categories.
Data Analytics
DOWNLOAD
Author : Juan J. Cuadrado-Gallego
language : en
Publisher:
Release Date : 2023
Data Analytics written by Juan J. Cuadrado-Gallego and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
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.
Principles Of Data Science
DOWNLOAD
Author : Ambrish Kumar Sharma
language : en
Publisher: AG PUBLISHING HOUSE (AGPH Books)
Release Date : 2022-10-19
Principles Of Data Science written by Ambrish Kumar Sharma and has been published by AG PUBLISHING HOUSE (AGPH Books) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Study Aids categories.
With the advent of the "big data" era, the necessity for secure data storage has risen. To solve the problem of data storage, the main emphasis was on building a framework. The key ingredient is data science. Data Science is an interdisciplinary field that applies statistical methods, computer science, and other disciplines to raw data to conclude the world. Data is a crucial part of every business since it provides the information upon which wise business choices may be made. To deal with the growing volume of data, the interdisciplinary subject of data science emerged. It employs rigorous methods, protocols, algorithms, and frameworks from the scientific community to mine vast stores of data for useful information. Both structured and unstructured information may be extracted. To understand and analyse real-world events using data, a field known as "data science" has emerged to bring together concepts, data analysis, Machine Learning, and related methodologies. Data science is a term for a wide range of subfields within the study of data analysis. Data Science is a broad discipline that draws upon many disciplines' theories, practices, and tools, including but not limited to statistics, information science, mathematics, and computer science. Data scientists use many different methods, such as machine learning, data visualization, pattern recognition, probability modelling, signal processing, data engineering, etc