Data Science In Practice
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Data Science
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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
Data Science In Practice
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Author : Alan Said
language : en
Publisher: Springer
Release Date : 2018-09-19
Data Science In Practice written by Alan Said and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-19 with Computers categories.
This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.
Data Science In Practice
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Author : Tom Alby
language : en
Publisher: CRC Press
Release Date : 2023-09-22
Data Science In Practice written by Tom Alby and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-22 with Mathematics categories.
Data Science in Practice is the ideal introduction to data science. With or without math skills, here, you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be. You will get to know the relevant analysis methods, and will be introduced to the programming language R, which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough, this book also deals with problems in project implementation, illuminates various fields of application, and does not forget to address ethical aspects. Data Science in Practice includes many examples, notes on errors, decision-making aids, and other practical tips. This book is ideal as a complementary text for university students, and is a useful learning tool for those moving into more data-related roles. Key Features: Success factors and tools for all project phases Includes application examples for various subject areas Introduces many aspects of Data Science, from requirements analysis to data acquisition and visualization
Data Science In Theory And Practice
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Author : Maria Cristina Mariani
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-30
Data Science In Theory And Practice written by Maria Cristina Mariani and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-30 with Mathematics categories.
DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.
Mastering Data Science Comprehensive Practice Questions For Certification Computational Graph Banyan Tree Colloborative Filtering Random Forest Cosine Distance Binary Tree
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date : 2024-04-16
Mastering Data Science Comprehensive Practice Questions For Certification Computational Graph Banyan Tree Colloborative Filtering Random Forest Cosine Distance Binary Tree written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-16 with Computers categories.
Mastering Data Science: Comprehensive Practice Questions for Certification" is a comprehensive guide designed to help aspiring data scientists prepare for certification exams. Authored by experts in the field, this book covers essential topics ranging from computational graphs to collaborative filtering, ensuring a thorough understanding of key concepts. The book begins by delving into computational graphs, providing detailed explanations and practice questions to reinforce learning. Readers learn how to construct and manipulate computational graphs, essential for understanding various machine learning algorithms. Next, the book explores the intricacies of the Banyan Tree algorithm, offering insights into its structure, operations, and applications in data science tasks. With practical examples and exercises, readers can master this powerful algorithm and its implementations. Collaborative filtering, another crucial aspect of data science, is thoroughly covered, with a focus on recommendation systems and user-item interactions. Readers gain a deep understanding of collaborative filtering techniques and their significance in personalized recommendation systems. Random Forest, a widely used ensemble learning method, is extensively discussed, with practice questions to solidify comprehension. Readers learn how Random Forest algorithms work, their advantages, and how to effectively implement them in various scenarios. Cosine distance, a fundamental concept in similarity measurement, is explored in detail, along with its applications in text mining, recommendation systems, and clustering algorithms. Lastly, the book covers binary trees, providing insights into their structure, traversal methods, and applications in data science. With comprehensive practice questions accompanying each topic, readers can assess their understanding and readiness for certification exams. Overall, "Mastering Data Science: Comprehensive Practice Questions for Certification" serves as an invaluable resource for aspiring data scientists, offering a thorough coverage of essential topics and ample opportunities for practice and self-assessment.
Fundamentals Of Data Science
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Author : Jugal K. Kalita
language : en
Publisher: Elsevier
Release Date : 2023-11-17
Fundamentals Of Data Science written by Jugal K. Kalita and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-17 with Mathematics categories.
Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. - Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning - Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning - Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis - Covers computer program code for implementing descriptive and predictive algorithms
Genetic Programming Theory And Practice Xiii
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Author : Rick Riolo
language : en
Publisher: Springer
Release Date : 2016-12-20
Genetic Programming Theory And Practice Xiii written by Rick Riolo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-20 with Computers categories.
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
Extending The Boundaries Of Design Science Theory And Practice
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Author : Bengisu Tulu
language : en
Publisher: Springer
Release Date : 2019-05-14
Extending The Boundaries Of Design Science Theory And Practice written by Bengisu Tulu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-14 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the 14th International Conference on Designing for a Digital and Globalized World, DESRIST 2019, held Worcester, MA, USA, June 2019. The 20 revised full papers included in the volume were carefully reviewed and selected from 54 submissions. They are organized in the following topical sections: Design Science Research Theory and Methodology; Design Science Research Applications in Healthcare; Design Science Research Applications in Data Science; and Design Science Research Applications in Emerging Topics.
Building An Effective Data Science Practice
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Author : Vineet Raina
language : en
Publisher:
Release Date : 2022
Building An Effective Data Science Practice written by Vineet Raina and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with 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. You will: 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 .
The Philosophy And Practice Of Science
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Author : David B. Teplow
language : en
Publisher: Cambridge University Press
Release Date : 2023-08-31
The Philosophy And Practice Of Science written by David B. Teplow and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-31 with Science categories.
This book is a novel synthesis of the philosophy and practice of science, covering its diverse theoretical, metaphysical, logical, philosophical, and practical elements. The process of science is generally taught in its empirical form: what science is, how it works, what it has achieved, and what it might achieve in the future. What is often absent is how to think deeply about science and how to apply its lessons in the pursuit of truth, in other words, knowing how to know. In this volume, David Teplow presents illustrative examples of science practice, history and philosophy of science, and sociological aspects of the scientific community, to address commonalities among these disciplines. In doing so, he challenges cherished beliefs and suggests to students, philosophers, and practicing scientists new, epistemically superior, ways of thinking about and doing science.