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Data Science
This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
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Essential GraphRAG
Upgrade your RAG applications with the power of knowledge graphs.Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM's training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Essential GraphRAG shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness.Inside Essential GraphRAG you'll learn: The benefits of using Knowledge Graphs in a RAG systemHow to implement a GraphRAG system from scratchThe process of building a fully working production RAG systemConstructing knowledge graphs using LLMsEvaluating performance of a RAG pipeline Essential GraphRAG is a practical guide to empowering LLMs with RAG. You'll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.
R 1,921.00
Data Science Projects with Python
Gain hands-on experience with industry-standard data analysis and machine learning tools in PythonKey FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook DescriptionData Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is forIf you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.
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Between Data Science and Applied Data Analysis
The volume presents new developments in data analysis and classification and gives an overview of the state of the art in these scientific fields and relevant applications. Areas that receive considerable attention in the book are clustering, discrimination, data analysis, and statistics, as well as applications in economics, biology, and medicine it provides recent technical and methodological developments and a large number of application papers demonstrating the usefulness of the newly developed techniques.
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Discovery Science
This book constitutes the proceedings of the 23rd International Conference on Discovery Science, DS 2020, which took place during October 19-21, 2020. The conference was planned to take place in Thessaloniki, Greece, but had to change to an online format due to the COVID-19 pandemic. The 26 full and 19 short papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data.
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Database Systems for Advanced Applications
This 6-volume set LNCS 16535-16540 constitutes the proceedings of the 31st International Conference on Database Systems for Advanced Applications, DASFAA 2026, held in Jeju, South Korea, at April 27-30, 2026. The 228 full papers presented in this book were carefully reviewed and selected from 834 submissions. They cover topics such as: Search and Recommendation Index and Storage Systems Graph and Social Network Machine Learning for Database Cloud Data Management Bio and Health Informatics Data-driven AI Data Quality and Credibility Text and Data Mining Data Mining and Knowledge Discovery Security, Privacy and Trust Information Extraction and Summarization Query Processing and Optimization RDF and Knowledge Graphs Distributed Ledger and Blockchain Industry Papers Demo Papers
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Partial Order Concepts in Applied Sciences
TThis book illustrates recent advances in applications of partial order theory and Hasse diagram techniques to data analysis, mainly in the socio-economic and environmental sciences. For years, partial order theory has been considered a fundamental branch of mathematics of only theoretical interest. In recent years, its effectiveness as a tool for data analysis is increasingly being realized and many applications of partially ordered sets to real problems in statistics and applied sciences have appeared. Main examples pertain to the analysis of complex and multidimensional systems of ordinal data and to problems of multi-criteria decision making, so relevant in social and environmental sciences. Partial Order Concepts in Applied Sciences presents new theoretical and methodological developments in partial order for data analysis, together with a wide range of applications to different topics: multidimensional poverty, economic development, inequality measurement, ecology and pollution, and biology, to mention a few. The book is of interest for applied mathematicians, statisticians, social scientists, environmental scientists and all those aiming at keeping pace with innovation in this interesting, growing and promising research field.
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Applications of Combinatorics and Graph Theory to the Biological and Social Sciences
This IMA Volume in Mathematics and its Applications Applications of Combinatorics and Graph Theory to the Biological and Social Sciences is based on the proceedings of a workshop which was an integral part of the 1987-88 IMA program on APPLIED COMBINATORICS. We are grateful to the Scientific Committee: Victor Klee (Chairman), Daniel Kleitman, Dijen Ray-Chaudhuri and Dennis Stanton for planning and implementing an exciting and stimulating year long program. We especially thank the Workshop Organizers, Joel Cohen and Fred Roberts, for organizing a workshop which brought together many of the major figures in a variety of research fields connected with the application of combinatorial ideas to the social and biological sciences. A vner Friedman Willard Miller APPLICATIONS OF COMBINATORICS AND GRAPH THEORY TO THE BIOLOGICAL AND SOCIAL SCIENCES: SEVEN FUNDAMENTAL IDEAS FRED S. RoBERTS* Abstract. To set the stage for the other papers in this volume, seven fundamental concepts which arise in the applications of combinatorics and graph theory in the biological and social sciences are described. These ideas are: RNA chains as "words" in a 4 letter alphabet; interval graphs; competition graphs or niche overlap graphs; qualitative stability; balanced signed graphs; social welfare functions; and semiorders. For each idea, some basic results are presented, some recent results are given, and some open problems are mentioned.
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Mathematical Introduction to Data Science
This textbook provides a comprehensive foundation in the mathematics needed for data science for students and self-learners with a basic mathematical background who are interested in the principles behind computational algorithms in data science. It covers sets, functions, linear algebra, and calculus, and delves deeply into probability and statistics, which are key areas for understanding the algorithms driving modern data science applications. Readers are guided toward unlocking the secrets of algorithms like Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression, illuminating the path from mathematical principles to algorithmic mastery.It is designed to make the material accessible and engaging, guiding readers through a step-by-step progression from basic mathematical concepts to complex data science algorithms. It stands out for its emphasis on worked examples and exercises that encourage active participation, making it particularly beneficial for those with limited mathematical backgrounds but a strong desire to learn. This approach facilitates a smoother transition into more advanced topics.The authors expect readers to be proficient in handling numbers in various formats, including fractions, decimals, percentages, and surds. They should also have a knowledge of introductory algebra, such as manipulating simple algebraic expressions, solving simple equations, and graphing elementary functions, along with a basic understanding of geometry including angles, trigonometry and Pythagoras’ theorem.
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Applied Regression Including Computing and Graphics
A step-by-step guide to computing and graphics in regression analysis In this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis. In 23 concise, easy-to-digest chapters, the authors present:? A wealth of simple 2D and 3D graphical techniques, helping visualize results through graphs * An improved version of the user-friendly Arc software, which lets readers promptly implement new ideas * Complete coverage of regression models, including logistic regression and generalized linear models * More than 300 figures, easily reproducible on the computer * Numerous examples and problems based on real data * A companion Web site featuring free software and advice, available at www.wiley.com/mathem atics Accessible, self-contained, and fully referenced, Applied Regression Including Computing and Graphics assumes only a first course in basic statistical methods and provides a bona fide user manual for the Arc software. It is an invaluable resource for anyone interested in learning how to analyze regression problems with confidence and depth.
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Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
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Applied modelling and computing in social science
In social science outstanding results are yielded by advanced simulation methods, based on state of the art software technologies and an appropriate combination of qualitative and quantitative methods. This book presents examples of successful applications of modelling and computing in social science: business and logistic process simulation and optimization, deeper knowledge extractions from big data, better understanding and predicting of social behaviour and modelling health and environment changes.
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Applied Graph Theory in Computer Vision and Pattern Recognition
This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.
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Advances in Knowledge Discovery and Data Mining
The five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10–13, 2025.The conference received a total of 557 submissions to the main track, 35 submissions to the survey track and 104 submittion to the special track on LLMs. Of these, 134 papers have been accepted for the main track, 10 for the survey track and 24 for the LLM track. 68 papers have been transferred to the4 DSFA special session. The papers have been organized in topical sections as follows: Part I: Anomaly Detection; Business Data Analysis; Clustering; Continual Learning; Contrastive Learning; Data Processing for Learning; Part II: Fairness and Interpretability; Federated Learning; Graph Mining and GNN; Learning on Scientific Data; Part III: Machine Learning; Multi-modality; OOD and Optimization; Recommender Systems; Representation Learning and Generative AI; Part IV: Security and Privacy; Temporal Learning; Survey; Part V: LLM Fine-tuning and Prompt Engineering; Fairness and Interpretability of LLMs; LLM Application; OOD and Optimization of LLMs.
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Opportunities and Challenges for Computational Social Science Methods
We are living in a digital era in which most of our daily activities take place online. This has created a big data phenomenon that has been subject to scientific research with increasingly available tools and processing power. As a result, a growing number of social science scholars are using computational methods for analyzing social behavior. To further the area, these evolving methods must be made known to sociological research scholars. Opportunities and Challenges for Computational Social Science Methods focuses on the implementation of social science methods and the opportunities and challenges of these methods. This book sheds light on the infrastructure that should be built to gain required skillsets, the tools used in computational social sciences, and the methods developed and applied into computational social sciences. Covering topics like computational communication, ecological cognition, and natural language processing, this book is an essential resource for researchers, data scientists, scholars, students, professors, sociologists, and academicians.
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Advances in Knowledge Discovery and Data Mining
The five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10–13, 2025.The conference received a total of 557 submissions to the main track, 35 submissions to the survey track and 104 submittion to the special track on LLMs. Of these, 134 papers have been accepted for the main track, 10 for the survey track and 24 for the LLM track. 68 papers have been transferred to the4 DSFA special session. The papers have been organized in topical sections as follows: Part I: Anomaly Detection; Business Data Analysis; Clustering; Continual Learning; Contrastive Learning; Data Processing for Learning; Part II: Fairness and Interpretability; Federated Learning; Graph Mining and GNN; Learning on Scientific Data; Part III: Machine Learning; Multi-modality; OOD and Optimization; Recommender Systems; Representation Learning and Generative AI; Part IV: Security and Privacy; Temporal Learning; Survey; Part V: LLM Fine-tuning and Prompt Engineering; Fairness and Interpretability of LLMs; LLM Application; OOD and Optimization of LLMs.
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Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science
The second of a two volume set on novel methods in harmonic analysis, this book draws on a number of original research and survey papers from well-known specialists detailing the latest innovations and recently discovered links between various fields. Along with many deep theoretical results, these volumes contain numerous applications to problems in signal processing, medical imaging, geodesy, statistics, and data science. The chapters within cover an impressive range of ideas from both traditional and modern harmonic analysis, such as: the Fourier transform, Shannon sampling, frames, wavelets, functions on Euclidean spaces, analysis on function spaces of Riemannian and sub-Riemannian manifolds, Fourier analysis on manifolds and Lie groups, analysis on combinatorial graphs, sheaves, co-sheaves, and persistent homologies on topological spaces. Volume II is organized around the theme of recent applications of harmonic analysis to function spaces, differential equations, and data science, covering topics such as:The classical Fourier transform, the non-linear Fourier transform (FBI transform), cardinal sampling series and translation invariant linear systems.Recent results concerning harmonic analysis on non-Euclidean spaces such as graphs and partially ordered sets.Applications of harmonic analysis to data science and statisticsBoundary-value problems for PDE's including the Runge–Walsh theorem for the oblique derivative problem of physical geodesy.
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Basketball Data Science
Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features:One of the first books to provide statistical and data mining methods for the growing field of analytics in basketballPresents tools for modelling graphs and figures to visualize the dataIncludes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test caseProvides the source code and data so readers can do their own analyses on NBA teams and players
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Data Science
Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.
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Building Neo4j-Powered Applications with LLMs
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilitiesKey FeaturesDesign vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4jApply best practices for graph exploration, modeling, reasoning, and performance optimizationBuild and consume Neo4j knowledge graphs and deploy your GenAI apps to Google CloudPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEmbark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j.As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI’s most persistent challenges—mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses.Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you’ll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud.By the end of this book, you’ll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications.What you will learnDesign, populate, and integrate a Neo4j knowledge graph with RAGModel data for knowledge graphsIntegrate AI-powered search to enhance knowledge explorationMaintain and monitor your AI search application with HaystackUse LangChain4j and Spring AI for recommendations and personalizationSeamlessly deploy your applications to Google Cloud PlatformWho this book is forThis LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy.
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Health Information Science
This book constitutes the refereed proceedings of the 11th International Conference onHealth Information Science, HIS 2022, held in Virtual Event during October 28–30, 2022.The 20 full papers and 9 short papers included in this book were carefully reviewed andselected from 54 submissions. They were organized in topical sections as follows: applications of health and medical data; health and medical data processing; health and medical data mining via graph-based approaches; and health and medical data classification.
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Case Studies in Applied Bayesian Data Science
Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution.The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.
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Advances in Data Science and Optimization of Complex Systems
This proceeding set contains 81 selected full papers presented at the International Conference on Applied Mathematics and Computer Science (ICAMCS 2024), which was held on December 20-21, 2024 in Hanoi, Vietnam, in honor of Professors Pham Dinh Tao and Le Thi Hoai An for the 40th birthday of DC (Difference of Convex functions) programming and DCA (DC Algorithm). The book covers theoretical and algorithmic as well as practical issues connected with several domains of Applied Mathematics and Computer Science, especially Optimization and Data Science. The present part I of the 2-volume set includes articles devoted to Mathematical programming and optimization, DC Programming & DCA, Operations research and decision making, Economics & Finance, Engineering Systems, Autonomous systems, Information theory and Information security. Researchers and practitioners in related areas will find a wealth of inspiring ideas and useful tools and techniques for their own work.
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Proceedings of the 5th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in Information Technology, Computer Science, and Mathematics. It covers fundamental and advanced concepts related to intelligent computing paradigms, data sciences, graph theory, and mathematical modeling. In high-performance computing, the need for intelligent, adaptive computing mechanisms and the integration of mathematical modeling in computational algorithms is becoming increasingly significant. Serving as a valuable resource for industry professionals, this book also supports beginners in gaining insights into enhanced computing paradigms and mathematical concepts, from foundational to advanced levels. Our objective is to provide a platform for researchers, engineers, academicians, and industry experts worldwide to share their findings on emerging trends. We believe this book not only presents innovative ideas but also fosters engaging discussions and inspires new perspectives.
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Data Science: From Research to Application
This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well as Internet of Things in the context of Industry 4.0.Data science is a rapidly growing field and, as a profession, incorporates a wide variety of areas, from statistics, mathematics and machine learning, to applied big data analytics. According to Forbes magazine, “Data Science” was listed as LinkedIn’s fastest-growing job in 2017.This book presents selected papers from the International Conference on Contemporary Issues in Data Science (CiDaS 2019), a professional data science event that provided a real workshop (not “listen-shop”) where scientists and scholars had the chance to share ideas, form new collaborations, and brainstorm on major challenges; and where industry experts could catch up on emerging solutions to help solve their concrete data science problems.Given its scope, the book will benefit not only data scientists and scientists from other domains, but also industry experts, policymakers and politicians.
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Graph-Theoretic Concepts in Computer Science
This book constitutes the revised selected papers of the 37th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2011, held at Teplá Monastery, Czech Republic, in June 2011.The 28 revised papers presented were carefully reviewed and selected from 52 submissions. The workshop aims at merging theory and practice by demonstrating how concepts from graph theory can be applied to various areas in computer science, and by extracting new graph theoretic problems from applications.
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Data Science
This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science.Key Features• Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science.• Presents predictive outcomes by applying data science techniques to real-life applications.• Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods.• Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields.The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.
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Advanced Interdisciplinary Applications of Deep Learning for Data Science
Advanced interdisciplinary applications of deep learning are revolutionizing the field of data science, enabling new solutions across industries. By leveraging neural networks and datasets, deep learning techniques are transforming how businesses analyze, interpret, and gain insights from complex data. In fields like healthcare, finance, engineering, and business, deep learning models can uncover patterns and make predictions to enhance data-driven decision-making while fostering new opportunities in areas like personalized medicine, autonomous systems, and predictive analytics. As deep learning continues evolves, its interdisciplinary impact expands, creating new possibilities for solving challenges in data science. Advanced Interdisciplinary Applications of Deep Learning for Data Science explores innovations of deep learning technology in data science for various industries and sectors. It examines solutions for decision-making and patient or customer service using machine learning, AI, and predictive analytics. This book covers topics such as bioinformatics, environmental monitoring, and medical diseases, and is a useful resource for data scientists, computer engineers, medical professionals, academicians, and researchers.
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Trends and Applications in Knowledge Discovery and Data Mining
This book constitutes the refereed proceedings of five workshops that were held in conjunction with the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10–13, 2025.The 31 papers presented in here were carefully reviewed and selected from 54 submissions. This volume includes the proceedings from the following Workshops:Workshop on Advanced Data-Driven Techniques for Urban Resilience (ADUR 2025)Workshop of Foundational AI for Pervasive Computing (FairPC 2025)Workshop on Graph Learning with Foundation Models (GLFM 2025)Workshop on Pattern Mining and Machine Learning for Bioinformatics (PM4B 2025)Workshop on Research and Applications of Foundation Models for Data Mining and Affective Computing (RAFDA 2025)
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Applied Statistics
This work has been thoughtfully designed so that it serves equally well as a reference for the practitioner and as a self-contained textbook for the advanced student. * Rewritten to maintain clarity and brevity while expanding the coverage of previous editions. * Changes to design-related topics include increased discussion of mixed models and random effects, greater emphasis on regression and data screening, and more use of graphs throughout. * Includes both graded and challenging exercises. * Liberal computer discussions now supplemented with SAS and SPSS.
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Graph-Theoretic Concepts in Computer Science
The 29th International Workshop on Graph-Theoretic Concepts in Computer Science(WG2003)washeldintheMennorodeconferenceCenterinElspeet,The Netherlands.TheworkshopwasorganizedbytheCenterforAlgorithmicSystems of the Institute of Information and Computing Sciences of Utrecht University. The workshop took place June 19–21, 2003. The 72 participants of WG 2003 came from universities and research institutes from 18 di?erent countries and ?ve di?erent continents. The workshop looks back at a long tradition. It was ?rst held in 1975, and has been held 20 times in Germany, twice in Austria, and once in Italy, Slo- kia, Switzerland, and the Czech Republic, and has now been held for the third time in The Netherlands. The workshop aims at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in computerscience,orbyextractingnewproblemsfromapplications.Itisdevoted to the theoretical and practical aspects of graph concepts in computer science. The goal is to present recent research results and to identify and explore - rections of future research. The talks given at the workshop showed how recent research results from algorithmic graph theory can be used in computer science and which graph-theoretic questions arise from new developments in computer science.
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Data Science with Java
Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications.Examine methods for obtaining, cleaning, and arranging data into its purest formUnderstand the matrix structure that your data should takeLearn basic concepts for testing the origin and validity of dataTransform your data into stable and usable numerical valuesUnderstand supervised and unsupervised learning algorithms, and methods for evaluating their successGet up and running with MapReduce, using customized components suitable for data science algorithms
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Data Science on the Google Cloud Platform
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way.You'll learn how to:Employ best practices in building highly scalable data and ML pipelines on Google CloudAutomate and schedule data ingest using Cloud RunCreate and populate a dashboard in Data StudioBuild a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQueryConduct interactive data exploration with BigQueryCreate a Bayesian model with Spark on Cloud DataprocForecast time series and do anomaly detection with BigQuery MLAggregate within time windows with DataflowTrain explainable machine learning models with Vertex AIOperationalize ML with Vertex AI Pipelines
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Graph Transformations in Computer Science
The research area of graph grammars and graph transformations dates back only two decades. But already methods and results from the area of graph transformation have been applied in many fields of computer science, such as formal language theory, pattern recognition and generation, compiler construction, software engineering, concurrent and distributed systems modelling, and database design and theory. This volume contains 24 selected and revised papers from an international seminar held in Dagstuhl, Germany, in 1993. The papers cover topics in the following areas: foundations of graph grammars and transformations; and applications of graph transformations to concurrent computing, specification and programming, and pattern generation and recognition.
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Transactions on Large-Scale Data- and Knowledge-Centered Systems LIX
The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 59th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains four fully revised and extended papers selected from the 40th conference on Data Management - Principles, Technologies and Applications, BDA 2024. The topics cover a wide range of timely data management research topics on graph optimization for information exposure, sequence similarity search, multi-model systems, and data privacy.
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Graph-Theoretic Concepts in Computer Science
This book constitutes the revised papers of the 45th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2019, held in Vall de Núria, Spain, in June 2019.The 29 full papers presented in this volume were carefully reviewed and selected from 87 submissions. They cover a wide range of areas, aiming at connecting theory and applications by demonstrating how graph-theoretic concepts can be applied in various areas of computer science. Another focus is on presenting recent results and on identifying and exploring promising directions of future research.
R 2,956.12
Advanced Data Science and Analytics with Python
The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today’s AI-driven world.
R 2,528.00
Graph-Theoretic Concepts in Computer Science
The 31st International Workshop on Graph-Theoretic Concepts in Computer Science (WG 2005) was held on the campus "Ile du Saulcy" of the Univ- sity Paul Verlaine-Metz in France. The workshop was organized by the La- ratoire d'Informatique Th' eorique et Appliqu' ee (LITA) and it took place June 23 - 25 2005. The 94 participants of WG 2005 came from universities and - search institutes of 18 di?erent countries. The WG 2005 workshop continues the series of 30 previous WG workshops. Since 1975,WG has taken place 20 times in Germany, four times in The Neth- lands,twotimesinAustriaaswellasonceinItaly,inSlovakia,inSwitzerlandand inCzechRepublic, andhas nowbeen heldfor the ?rsttime inFrance.The wo- shop aims at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in computer science, or by extracting new problems from applications. The goal is to present recent research results and to identify and explore directions of future research. The talks were given in the "Petit Th' eatre".They showed how recent research results from algori- mic graph theory can be used in computer science and which graph-theoretic questions arise from new developments in computer science. There were two fascinating invited lectures by Georg Gottlob (Vienna, Austria) and Gregory Kucherov (Nancy, France).
R 1,587.07
Graph-Theoretic Concepts in Computer Science
This volume constitutes the thoroughly refereed proceedings of the 49th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2023. The 33 full papers presented in this volume were carefully reviewed and selected from a total of 116 submissions. The WG 2022 workshop aims to merge theory and practice by demonstrating how concepts from graph theory can be applied to various areas in computer science, or by extracting new graph theoretic problems from applications.
R 3,540.81
