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Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2nd Edition
by Dávid NatinggaBuild a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook DescriptionMachine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problemWhat you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is forThis book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set
Data Science Algorithms in a Week
by David NatinggaBuild strong foundation of machine learning algorithms In 7 days. About This Book • Get to know seven algorithms for your data science needs in this concise, insightful guide • Ensure you're confident in the basics by learning when and where to use various data science algorithms • Learn to use machine learning algorithms in a period of just 7 days Who This Book Is For This book is for aspiring data science professionals who are familiar with Python and have a statistics background. It is ideal for developers who are currently implementing one or two data science algorithms and want to learn more to expand their skill set. What You Will Learn • Find out how to classify using Naive Bayes, Decision Trees, and Random Forest to achieve accuracy to solve complex problems • Identify a data science problem correctly and devise an appropriate prediction solution using Regression and Time-series • See how to cluster data using the k-Means algorithm • Get to know how to implement the algorithms efficiently in the Python and R languages In Detail Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis. This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets. This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem. Style and approach Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.
Data Science Analytics and Applications: First International Conference, Dasaa 2017, Chennai, India, January 4-6, 2017, Revised Selected Papers (Communications In Computer And Information Science #804)
by Shriram R Mak SharmaThis book constitutes the refereed proceedings of the First International Conference on Data Science Analytics and Applications, DaSAA 2017, held in Chennai, India, in January 2017. The 16 revised full papers and 4 revised short papers presented were carefully reviewed and selected from 77 submissions. The papers address issues such as data analytics, data mining, cloud computing, machine learning, text classification and analysis, information retrieval, DSS, security, image and video processing.
Data Science and Algorithms in Systems: Proceedings of 6th Computational Methods in Systems and Software 2022, Vol. 2 (Lecture Notes in Networks and Systems #597)
by Radek Silhavy Petr Silhavy Zdenka ProkopovaThis book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured. This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings.
Data Science and Analytics: 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part II (Communications in Computer and Information Science #1230)
by Usha Batra Nihar Ranjan Roy Brajendra PandaThis two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.
Data Science and Analytics: 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I (Communications in Computer and Information Science #1229)
by Usha Batra Nihar Ranjan Roy Brajendra PandaThis two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.
Data Science and Analytics: 4th International Conference On Recent Developments In Science, Engineering And Technology, Redset 2017, Gurgaon, India, October 13-14, 2017, Revised Selected Papers (Communications In Computer And Information Science #799)
by Brajendra Panda Sudeep Sharma Nihar Ranjan RoyThis book constitutes the refereed proceedings of the 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, held in Gurgaon, India, in October 2017. The 66 revised full papers presented were carefully reviewed and selected from 329 submissions. The papers are organized in topical sections on big data analysis, data centric programming, next generation computing, social and web analytics, security in data science analytics.
Data Science and Analytics for SMEs: Consulting, Tools, Practical Use Cases
by Afolabi Ibukun TolulopeMaster the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain.This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly. Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business. What You'll LearnCreate and measure the success of their analytics projectStart your business analytics consulting careerUse solutions taught in the book in practical uses cases and problems Who This Book Is ForBusiness analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts
Data Science and Analytics Strategy: An Emergent Design Approach (Chapman & Hall/CRC Data Science Series)
by Kailash Awati Alexander ScrivenThis book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls.
Data Science and Analytics with Python (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Jesus Rogel-SalazarData Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.
Data Science and Applications: Proceedings of ICDSA 2023, Volume 3 (Lecture Notes in Networks and Systems #820)
by Satyasai Jagannath Nanda Rajendra Prasad Yadav Amir H. Gandomi Mukesh SaraswatThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2023), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 14 to 15 July 2023. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Data Science and Applications: Proceedings of ICDSA 2023, Volume 2 (Lecture Notes in Networks and Systems #819)
by Satyasai Jagannath Nanda Rajendra Prasad Yadav Amir H. Gandomi Mukesh SaraswatThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2023), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 14 to 15 July 2023. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Data Science and Applications: Proceedings of ICDSA 2023, Volume 1 (Lecture Notes in Networks and Systems #818)
by Satyasai Jagannath Nanda Rajendra Prasad Yadav Amir H. Gandomi Mukesh SaraswatThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2023), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 14 to 15 July 2023. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Data Science and Applications: Proceedings of ICDSA 2023, Volume 4 (Lecture Notes in Networks and Systems #821)
by Satyasai Jagannath Nanda Rajendra Prasad Yadav Amir H. Gandomi Mukesh SaraswatThis book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2023), organized by Soft Computing Research Society (SCRS) and Malaviya National Institute of Technology Jaipur, India, from 14 to 15 July 2023. The book is divided into four volumes, and it covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Data Science and Applications for Modern Power Systems (Power Electronics and Power Systems)
by Le Xie Yang Weng Ram RajagopalThis book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid.
Data Science and Artificial Intelligence: First International Conference, DSAI 2023, Bangkok, Thailand, November 27–29, 2023, Proceedings (Communications in Computer and Information Science #1942)
by Chutiporn Anutariya Marcello M. BonsangueThis book constitutes the proceedings of the First International Conference, DSAI 2023, held in Bangkok, Thailand, during November 27–30, 2023. The 22 full papers and the 4 short papers included in this volume were carefully reviewed and selected from 70 submissions. This volume focuses on ideas, methodologies, and cutting-edge research that can drive progress and foster interdisciplinary collaboration in the fields of data science and artificial intelligence.
Data Science and Artificial Intelligence: Second International Conference, DSAI 2024, Medan, Indonesia, November 13–15, 2024, Proceedings (Communications in Computer and Information Science #2318)
by Chutiporn Anutariya Marcello M. Bonsangue Erna Budhiarti-Nababan Opim Salim SitompulThis book constitutes the proceedings of the Second refereed proceedings of the Second International Conference on Data Science and Artificial Intelligence, DSAI 2024, held in Medan, Indonesia, during November 13–15, 2024. The 18 full papers, 2 short papers and 3 invited talks were included in this book were carefully reviewed and selected from 69 submissions. They are organized in the following topical sections: Keynote Presentations; Natural and Sign Language Processing; Applications of Data Science and Artificial Intelligence; Affective Computing and AI Games; Embedded AI and Applications; Data Science; AI and Healthcare.
Data Science and Artificial Intelligence for Digital Healthcare: Communications Technologies for Epidemic Models (Signals and Communication Technology)
by Fionn Murtagh Marcello Trovati Mohammed Atiquzzaman Pradeep Kumar Singh Mohsen FaridThis book explores current research and development in the area of digital healthcare using recent technologies such as data science and artificial intelligence. The authors discuss how data science, AI, and mobile technologies provide the fundamental backbone to digital healthcare, presenting each technology separately as well covering integrated solutions. The book also focuses on the integration of different multi-disciplinary approaches along with examples and case studies. In order to identify the challenges with security and privacy issues, relevant block chain technologies are identified and discussed. Social aspects related to digital solutions and platforms for healthcare are also discussed and analyzed. The book aims to present high quality, technical contributions in the field of mobile digital healthcare using technologies such as AI, deep learning, IoT and distributed cloud computing.
Data Science and Big Data: An Environment of Computational Intelligence (Studies in Big Data #24)
by Witold Pedrycz Shyi-Ming ChenThis book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business. Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy. Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs. The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.
Data Science and Big Data Analytics: ACM-WIR 2018 (Lecture Notes on Data Engineering and Communications Technologies #16)
by Durgesh Kumar Mishra Xin-She Yang Aynur UnalThis book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.
Data Science and Big Data Analytics: Proceedings of IDBA 2023 (Data-Intensive Research)
by Durgesh Mishra Xin She Yang Aynur Unal Dharm Singh JatThis book features high-quality research papers presented at the Third International Conference on Data Science and Big Data Analytics (IDBA 2023), organized by Sri Aurobindo Institute of Technology, Indore, India, in association with ACM and IEEE Computer Society in hybrid mode during June 16–17, 2023. This book discusses the topics such as data science, artificial intelligence, machine learning, quantum computing, big data and cloud security, computation security, big data security, information security, forecasting, data analytics, mathematics for data science, graph theory and application in data science, data visualization, computer vision, and analytics for social networks.
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
by Emc Education ServicesData Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.This book will help you:Become a contributor on a data science teamDeploy a structured lifecycle approach to data analytics problemsApply appropriate analytic techniques and tools to analyzing big dataLearn how to tell a compelling story with data to drive business actionPrepare for EMC Proven Professional Data Science Certification
Data Science and Big Data Analytics in Smart Environments
by Marta Chinnici; Florin Pop; Cătălin NegruMost applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment.Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
Data Science and Big Data Computing: Frameworks and Methodologies
by Zaigham MahmoodThis 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.
Data Science and Communication: Proceedings of ICTDsC 2023 (Studies in Autonomic, Data-driven and Industrial Computing)
by João Manuel R. S. Tavares Joel J. P. C. Rodrigues Debajyoti Misra Debasmriti BhattacherjeeThe book presents selected papers from the International Conference on Data Science and Communication (ICTDsC 2023) organized by the Department of Electronics and Communication Engineering and Department of Engineering Science and Humanities (DESH) Siliguri Institute of Technology, India during 23 – 24 March 2023 in Siliguri, India. The book covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cyber security and forensic, network and mobile security, advanced computing, cloud computing, quantum computing, electronics system, Internet of Things, robotics and automations, blockchain and software technology, and digital technologies for future.