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Data Science
This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015.
R 5,027.70
Data Science
This book constitutes the refereed proceedings of theSecond International Conference on Data Science, ICDS 2015, held in Sydney,Australia, during August 8-9, 2015.The 19 revised full papers and 5 short papers presentedwere carefully reviewed and selected from 31 submissions. The papers focus onthe following topics: mathematical issues in data science; big data issues andapplications; data quality and data preparation; data-driven scientificresearch; evaluation and measurement in data service; big data mining andknowledge management; case study of data science; social impacts of datascience.
R 2,158.32
Data Modeling with Snowflake
Learn to build scalable data architecture using Snowflake's unique features through real-world use cases and SQL recipes that unlock data value for business and engineering teamsKey FeaturesLearn core modeling techniques through practical examples using native Snowflake architectureAdopt a universal modeling language to communicate business value to functional teamsGo beyond physical modeling with SQL recipes to transform and shape your Snowflake dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionStruggling with rising Snowflake costs and constant tuning? Poorly aligned data models can lead to bloated expenses, inefficient queries, and time-consuming rework. Data Modeling with Snowflake helps you harness the Snowflake Data Cloud’s scalable, cloud-native architecture and expansive feature set to deliver data solutions faster than ever.This book introduces simple, practical data modeling frameworks that accelerate agile design and evolve alongside your projects from concept to code. Rooted in decades of proven database design principles, these frameworks are paired, for the first time, with Snowflake-native objects and real-world examples, offering a two-in-one crash course in theory and direct application.Through real-world examples designed to make learning easy, you’ll leverage Snowflake’s innovative features like Time Travel, Zero-Copy Cloning, and Change Data Capture (CDC) to create cost-efficient solutions. Whether you're just starting out or refining your architecture, this book will guide you in designing smarter, scaling faster, and cutting costs by aligning timeless modeling principles with the power of Snowflake.What you will learnMaster data modeling fundamentals to get your designs right on the first attempt with time-saving benefitsDiscover Snowflake's cloud-native architecture and unique featuresApply modeling concepts to achieve higher efficiency by leveraging Snowflake architectureGet to grips with modeling concepts like normalization and slowly changing dimensions (SCDs)Read and transform semi-structured data, including hierarchies, using pre-built recipes and examplesExplore organizational frameworks like Data Vault and Data MeshUnderstand the business metrics that drive scalable, cost-effective data projectsWho this book is forWhether building data products or managing a data platform at scale, this book serves any developer who jumped into SQL without first establishing modeling best practices and understanding their effective applications to save time and effort.For analysts or those starting in database design, this book provides practical foundations of data modeling to guide you on your Snowflake data journey.Those new to Snowflake will discover its core features and learn to incorporate them into universal modeling frameworks.
R 1,954.00
Data Science
This three-volume set CCIS 2213-2215 constitutes the refereed proceedings of the 10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024, held in Macau, China, during September 27–30, 2024.The 74 full papers and 3 short papers presented in these three volumes were carefully reviewed and selected from 249 submissions.The papers are organized in the following topical sections:Part I: Novel methods or tools used in big data and its applications; applications of data science.Part II: Education research, methods and materials for data science and engine; data security and privacy; big data mining and knowledge management.Part III: Infrastructure for data science; social media and recommendation system; multimedia data management and analysis.
R 3,329.70
Java for Data Science
Examine the techniques and Java tools supporting the growing field of data scienceAbout This Book* Your entry ticket to the world of data science with the stability and power of Java* Explore, analyse, and visualize your data effectively using easy-to-follow examples* Make your Java applications more capable using machine learningWho This Book Is ForThis book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful.What You Will Learn* Understand the nature and key concepts used in the field of data science* Grasp how data is collected, cleaned, and processed* Become comfortable with key data analysis techniques* See specialized analysis techniques centered on machine learning* Master the effective visualization of your data* Work with the Java APIs and techniques used to perform data analysisIn DetailData science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation.The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book.Style and approachThis book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.
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Emerging Trends, Techniques, and Applications in Geospatial Data Science
With the emergence of smart technology and automated systems in today's world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.
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Data Science and Productivity Analytics
This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.
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Data Science with Semantic Technologies
DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? Audience Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.
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Blockchain Fundamentals, Data Structures, and Algorithms for Data Science
Offering a systematic exploration of blockchain networks from both technical and analytical viewpoints, this book introduces the core structures that underpin blockchain systems, transactions, addresses, and smart contracts and explains how these can be modeled, visualized, and analyzed using modern data science methods. Bridging computer science, finance, and statistics, it integrates algorithmic reasoning with economic intuition to study decentralization, risk, and trust in digital economies. Through examples drawn from Bitcoin, Ethereum, Ripple, Monero, Zcash, IOTA, and DeFi, readers learn how blockchain data can be transformed into graph and temporal models for fraud detection, systemic risk analysis, and network behavior prediction. Featuring clear explanations, illustrative figures, and Solidity code, this volume serves as an essential reference for students, researchers, and practitioners in finance, data science, statistics, machine learning, and distributed systems.
R 3,391.00
Data Science
This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application.
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Artificial Intelligence and Complex Dynamical Systems
This book serves as a comprehensive introduction to nonlinear complex systems through the application of machine learning methods. Artificial intelligence (AI) has affected the foundations of scientific discovery, and can therefore lend itself to developing a better understanding of the unpredictable nature of complex dynamical systems and to predict their future evolution. Utilizing Python code, this book teaches and applies machine learning to topics such as chaotic dynamics and time-series analysis, solitons, breathers, chimeras, nonlinear localization, biomolecular dynamics, and wave propagation in the heart. The consistent integration of methods and models allow for readers to develop a necessary intuition on how to handle complexity through AI. This textbook contains a wealth of expository material, code, and example problems to support and organize academic coursework, allowing the technical nature of these areas of study to become highly accessible. Requiring only a basic background in mathematics and coding in Python, this book is an essential text for a wide array of advanced undergraduate or graduate students in the applied sciences interested in complex systems through the lens of machine learning.
R 2,564.00
Data Engineering with Azure Databricks
Master end-to-end data engineering on Azure Databricks. From data ingestion and Delta Lake to CI/CD and real-time streaming, build secure, scalable, and performant data solutions with Spark, Unity Catalog, and ML tools.Key FeaturesBuild scalable data pipelines using Apache Spark and Delta LakeAutomate workflows and manage data governance with Unity CatalogLearn real-time processing and structured streaming with practical use casesImplement CI/CD, DevOps, and security for production-ready data solutionsExplore Databricks-native ML, AutoML, and Generative AI integrationBook Description"Data Engineering with Azure Databricks" is your essential guide to building scalable, secure, and high-performing data pipelines using the powerful Databricks platform on Azure. Designed for data engineers, architects, and developers, this book demystifies the complexities of Spark-based workloads, Delta Lake, Unity Catalog, and real-time data processing.Beginning with the foundational role of Azure Databricks in modern data engineering, you’ll explore how to set up robust environments, manage data ingestion with Auto Loader, optimize Spark performance, and orchestrate complex workflows using tools like Azure Data Factory and Airflow.The book offers deep dives into structured streaming, Delta Live Tables, and Delta Lake’s ACID features for data reliability and schema evolution. You’ll also learn how to manage security, compliance, and access controls using Unity Catalog, and gain insights into managing CI/CD pipelines with Azure DevOps and Terraform.With a special focus on machine learning and generative AI, the final chapters guide you in automating model workflows, leveraging MLflow, and fine-tuning large language models on Databricks. Whether you're building a modern data lakehouse or operationalizing analytics at scale, this book provides the tools and insights you need.What you will learnSet up a full-featured Azure Databricks environmentImplement batch and streaming ingestion using Auto LoaderOptimize Spark jobs with partitioning and cachingBuild real-time pipelines with structured streaming and DLTManage data governance using Unity CatalogOrchestrate production workflows with jobs and ADFApply CI/CD best practices with Azure DevOps and GitSecure data with RBAC, encryption, and compliance standardsUse MLflow and Feature Store for ML pipelinesBuild generative AI applications in DatabricksWho this book is forThis book is for data engineers, solution architects, cloud professionals, and software engineers seeking to build robust and scalable data pipelines using Azure Databricks. Whether you're migrating legacy systems, implementing a modern lakehouse architecture, or optimizing data workflows for performance, this guide will help you leverage the full power of Databricks on Azure. A basic understanding of Python, Spark, and cloud infrastructure is recommended.
R 1,628.00
Tensor Decompositions for Data Science
Tensors are essential in modern day computational and data sciences. This book explores the foundations of tensor decompositions, a data analysis methodology that is ubiquitous in machine learning, signal processing, chemometrics, neuroscience, quantum computing, financial analysis, social science, business market analysis, image processing, and much more. In this self-contained mathematical, algorithmic, and computational treatment of tensor decomposition, the book emphasizes examples using real-world downloadable open-source datasets to ground the abstract concepts. Methodologies for 3-way tensors (the simplest notation) are presented before generalizing to d-way tensors (the most general but complex notation), making the book accessible to advanced undergraduate and graduate students in mathematics, computer science, statistics, engineering, and physical and life sciences. Additionally, extensive background materials in linear algebra, optimization, probability, and statistics are included as appendices.
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Intelligent Systems in Healthcare and Disease Identification using Data Science
The health technology has become a hot topic in academic research. It employs the theory of social networks into the different levels of the prediction and analysis and has brought new possibilities for the development of technology. This book is a descriptive summary of challenges and methods using disease identification with various case studies from diverse authors across the globe. One of the new buzzwords in healthcare sector that has become popular over years is health informatics. Healthcare professionals must deal with an increasing number of computers and computer programs in their daily work. With rapid growth of digital data, the role of analytics in healthcare has created a significant impact on healthcare professional’s life. Improvements in storage data, computational power and paral- lelization has also contributed to uptake this technology. This book is intended for use by researchers, health informatics professionals, academicians and undergraduate and postgraduate students interested in knowing more about health informatics. It aims to provide a brief overview about informatics, its history and area of practice, laws in health informatics, challenges and technologies in health informatics, applica- tion of informatics in various sectors and so on. Finally, the research avenues in health informatics along with some case studies are discussed.
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Data Science for Effective Healthcare Systems
Data Science for Effective Healthcare Systems has a prime focus on the importance of data science in the healthcare domain. Various applications of data science in the health care domain have been studied to find possible solutions. In this period of COVID-19 pandemic data science and allied areas plays a vital role to deal with various aspect of health care. Image processing, detection & prevention from COVID-19 virus, drug discovery, early prediction, and prevention of diseases are some thrust areas where data science has proven to be indispensable.Key Features:The book offers comprehensive coverage of the most essential topics, including:Big Data Analytics, Applications & Challenges in HealthcareDescriptive, Predictive and Prescriptive Analytics in HealthcareArtificial Intelligence, Machine Learning, Deep Learning and IoT in HealthcareData Science in Covid-19, Diabetes, Coronary Heart Diseases, Breast Cancer, Brain TumorThe aim of this book is also to provide the future scope of these technologies in the health care domain. Last but not the least, this book will surely benefit research scholar, persons associated with healthcare, faculty, research organizations, and students to get insights into these emerging technologies in the healthcare domain.
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R 1,008.00
Data Science
This two volume set (CCIS 1628 and 1629) constitutes the refereed proceedings of the 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022 held in Chengdu, China, in August, 2022.The 65 full papers and 26 short papers presented in these two volumes were carefully reviewed and selected from 261 submissions. The papers are organized in topical sections on: Big Data Management and Applications; Data Security and Privacy; Applications of Data Science; Infrastructure for Data Science; Education Track; Regulatory Technology in Finance.
R 4,691.63
Simulation for Data Science with R
Harness actionable insights from your data with computational statistics and simulations using RAbout This Book• Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies• A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.What You Will Learn• The book aims to explore advanced R features to simulate data to extract insights from your data.• Get to know the advanced features of R including high-performance computing and advanced data manipulation• See random number simulation used to simulate distributions, data sets, and populations• Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations• Applications to design statistical solutions with R for solving scientific and real world problems• Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.In DetailData Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world.The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results.By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these
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Data Science, Learning by Latent Structures, and Knowledge Discovery
This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.
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Computational Thinking and Social Science
Whilst providing a fundamental understanding of computational social science, this book delves into the tools and techniques used to build familiarity with programming and gain context into how, why and when they are introduced. The overall focus is on helping you understand and design computational social science research, alongside delving into hands-on coding and technical instruction.Key features include: Further reading Exercises accompanied by sample code Programming examples in Scratch, Python and RKey concepts Chapter summaries With experience in course design and teaching, Matti Nelimarkka has a deep understanding of learning techniques within computational social sciences, with the main aim of blending researching, thinking and designing together to gain a grounded foundation for coding, programming, methodologies and key concepts.
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Computational Physics Using Python
This book provides a practical introduction to using computational (or numerical) methods to solve physics problems using the Python programming language, including differential equations, Fourier transforms, Monte Carlo methods, and data analysis.It is designed with a two-level approach: topics are introduced at the lowest level, and readers encounter the simplest examples of coding the algorithm themselves before a second level introduced by the problems allows the reader to use library models and take their understanding to a higher level.The book does not teach Python programming as students traditionally have already learnt those skills before studying computational methods, but it instead teaches readers to apply their knowledge to solve realistic physics problems.The book is aimed at advanced undergraduate or beginning graduate students in physics or engineering. A junior-level university (or college) physics and mathematics background is assumed. But readers will not be prevented from understanding or applying numerical methods because of a lack of knowledge in a specific physics area.Key features: Explores a wide spectrum of topics, from classical numerical methods to solving ordinary and partial differential equations of physics, plus spectral methods, data analysis, and Monte Carlo methods. Includes a chapter on data analysis and statistics, not traditionally covered in related titles on computational methods for scientists. Chapters are accompanied by problems and worked solutions (discussions, example code and output). Readers can access the full set of solutions under the support materials tab at: http://www.routledge.com/9781041116288.
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R 3,134.00
Quantitative Trading Strategies Using Python
Build and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python.Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part two introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part three covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach. Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you’ll come away from it with a firm understanding of core trading strategies and how to use Python to implement them.What You Will LearnMaster the fundamental concepts of quantitative tradingUse Python and its popular libraries to build trading models and strategies from scratchPerform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using PythonUtilize common trading strategies such as trend-following, momentum trading, and pairs tradingEvaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtestingWho This Book Is ForAspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields.
R 1,254.00
Data Science
This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mining and Knowledge Management, Infrastructure for Data Science, Machine Learning for Data Science, Data Security and Privacy, Applications of Data Science, Case Study of Data Science, Multimedia Data Management and Analysis, Data-driven Scientific Research, Data-driven Bioinformatics, Data-driven Healthcare, Data-driven Management, Data-driven eGovernment, Data-driven Smart City/Planet, Data Marketing and Economics, Social Media and Recommendation Systems, Data-driven Security, Data-driven Business Model Innovation, Social and/or organizational impacts of Data Science.
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Quantitative Social Science Data with R
Relevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs.Gradually introducing applied statistics and the language and functionality of R and R Studio software, it uses examples from across the social sciences to show students how to apply abstract statistical and methodological principles to their own work. Maintaining a student-friendly pace, it goes beyond a normal introductory statistics book and shows students where data originates and how to:- Understand and use quantitative data to answer questions- Approach surrounding ethical issues- Collect quantitative data- Manage, write about, and share the data effectivelySupported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives students not only the tools they need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what they have learned.
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Data Science MBA
This text book focuses on what could be the most important challenge for firms to boost long-term productivity and competitiveness: digital strategy. It seeks to provide readers with a solid knowledge of the most relevant issues and concepts, that will be relevant to MBA students in real-world settings. The book discusses theoretical concepts relating to digital strategy, while also using hands-on data analysis in R software to illustrate some fundamental features and pitfalls of working with real-world data. The book starts by clarifying the meaning of relevant concepts (digitization vs digitalization; Machine learning, Artificial Intelligence), presents three leading models of digital transformation, and explains how digitalization has far-reaching implications for how organizations need to be structured. Then the book discusses the skills of a data scientist, and how digital transformation leads to new concerns surrounding ethics. Other themes include data quality, data pre-processing, data visualization, as well as the distinction between prediction and causal inference. Many of these themes are illustrated using R examples, that familiarize the reader with data analysis, using these hands-on experiences to uniquely illustrate some important themes surrounding statistical analysis, and to let readers see for themselves how some popular statistical and data science techniques actually work.
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Statistical Learning and Data Science
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit.Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data.Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments.
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Revolutionizing Data Science and Analytics for Industry Transformation
The fields of data science and analytics are always evolving. By understanding and navigating the data-driven revolution, advancements can be made in domains such as healthcare, finance, education, and more. Harnessing the power of data in decision-making and problem-solving across industries, industries may advance their methods of data collection, data visualization, and predictive modeling. As such, it is important to stay abreast of the latest developments, methodologies, and trends in data-driven research. Revolutionizing Data Science and Analytics for Industry Transformation contributes to fostering collaboration, innovation, and excellence in data science research. It democratizes access to knowledge and empowers individuals and organizations to thrive in the data-driven era. Covering topics such as outcome prediction, deep reinforcement learning, and Mask Attention BERT Networks, this book is an excellent resource for data scientists, business professionals, policymakers, computer scientists, statisticians, engineers, professionals, researchers, scholars, academicians, and more.
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Student Guide to AI and Robotics
This book provides a practical and accessible introduction to AI and robotics, presenting the foundational technologies driving the development of intelligent systems. It covers key topics such as machine learning, perception, control, and autonomous decision-making, balancing theoretical concepts with practical applications.Through selected examples and case studies, the book explores robotics platforms and applied AI technologies used across industries, offering insights into their transformative potential in real-world scenarios. With a focus on hands-on learning, it introduces programming languages and tools commonly used in AI and robotics, empowering readers to apply concepts and techniques effectively. It also addresses the importance of building secure, responsible systems and examines the future of human-AI collaboration.An essential resource for introductory AI and robotics courses, this book provides the foundational knowledge needed to advance in these dynamic fields.
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Machine Learning For Dummies
The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithms With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.
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Data Science and Big Data Analytics
This 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.
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R 4,549.00
Silicon Valley Python Engineer Interview Guide
Silicon Valley Python Interview Guide: Data Structures, Algorithms, and System Design is an essential resource for aspiring software engineers preparing for technical interviews at top-tier companies. This book provides a comprehensive roadmap, covering foundational concepts, practical coding techniques, and advanced problem-solving strategies to help candidates excel in interviews. With a focus on Python, the book equips readers with the skills to tackle challenging coding problems, design scalable systems, and communicate solutions effectively. In the first half, the book delves into core data structures (lists, stacks, queues, graphs, and trees) and algorithms (binary search, dynamic programming, DFS, BFS, and backtracking),offering practical examples and Python implementations. The latter half transitions to system design, including big data architectures, distributed systems, and machine learning workflows. Case studies on real-world applications like Tiny URL, autocomplete systems, and Chat GPT-like models provide hands-on insights. Whether you are an early-career engineer or an experienced professional, this guide is designed to enhance your preparation with real-world examples, tested code, and proven strategies. It is more than a technical handbook—it is your roadmap to building confidence and securing a role in the competitive tech industry.
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Building an Effective Data Science Practice
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 scienceEvaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a projectBuild and operate an effective interdisciplinary data science team within an organizationEvaluating the progress of the team towards the business RoIUnderstand the important regulatory aspects that are applicable to a data science practice Who This Book Is ForTechnology leaders, data scientists, and project managers
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