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Geospatial Data Science
This introductory textbook teaches the simple development of geospatial applications based on the principles and software tools of geospatial data science. It introduces a new generation of geospatial technologies that have emerged from the development of the Semantic Web and the linked data paradigm, and shows how data scientists can use them to build environmental applications easily. Geospatial data science is the science of collecting, organizing, analyzing, and visualizing geospatial data. Since around 2010, there has been extensive work in the area of geospatial data science using semantic technologies and linked data, from researchers in the areas of the Semantic Web, Geospatial Databases and Geoinformatics. The main results of this research have been the publication of the OGC standard GeoSPARQL and the implementation of a number of linked data tools supporting this standard. Up to now, there has been no textbook that enables someone to teach this material to undergraduate or graduate students.The material of the book is developed in a tutorial style and it is appropriate for an introductory course on the subject. This can be an advanced undergraduate course or a graduate course offered by Computer Science or GIS faculty. It is a hands-on approach and every chapter contains exercises that help students master the material.The book is accompanied by a Web site where solutions to some of the exercises are given together with supplementary material such as datasets and code. Most of the material in the book has been tried in the "Knowledge Technologies" course taught by the first author in the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens since 2012.
R 1,830.00
Hands-On Artificial Intelligence for IoT
Master AI and IoT integration, from fundamentals to advanced techniques, and revolutionize your approach to building intelligent, data-driven solutions across industriesKey FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataEnhance your IoT solutions with advanced AI techniques, including deep learning, optimization, and generative adversarial networksGain practical insights through industry-specific IoT case studies in manufacturing, smart cities, and automationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTransform IoT devices into intelligent systems with this comprehensive guide by Amita Kapoor, Chief AI Officer at Tipz AI. Drawing on 25 years of expertise in developing intelligent systems across industries, she demonstrates how to harness the combined power of artificial intelligence and IoT technology. A pioneer in making AI and neuroscience education accessible worldwide, Amita guides you through creating smart, efficient systems that leverage the latest advances in both fields. This new edition is updated with various optimization techniques in IoT used for enhancing efficiency and performance. It introduces you to cloud platforms such as Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) for analyzing data generated using IoT devices. You’ll learn about machine learning algorithms, deep learning techniques, and practical applications in real-world IoT scenarios and advance to creating AI models that work with diverse data types, including time series, images, and audio. You’ll also harness the power of widely used Python libraries, TensorFlow and Keras, to build a variety of smart AI models. By the end of the book, you’ll emerge as a master of AI-driven IoT, armed with invaluable experience in optimizing IoT devices, boosting their performance, and integrating AI algorithms to make intelligent decisions.What you will learnIntegrate AI and IoT for enhanced device intelligenceUnderstand how to build scalable and efficient IoT systemsMaster both supervised and unsupervised machine learning techniques for processing IoT dataExplore the full potential of deep learning in IoT applicationsDiscover AI-driven strategies to optimize IoT system efficiencyImplement real-world IoT projects that leverage AI capabilitiesImprove device performance and decision-making using AI algorithmsWho this book is forThis book is for IoT developers, engineers, and tech enthusiasts, particularly those with a background in Python, looking to integrate artificial intelligence and machine learning into IoT systems. Python developers eager to apply their knowledge in new, innovative ways will find it useful. It’s also an invaluable guide for anyone with a foundational understanding of IoT concepts ready to take their skills to the next level and shape the future of intelligent devices.
R 1,465.00
R 1,238.00
Learning Tableau 2025
Explore the full potential of Tableau and learn how to draw valuable insights from data directly from a Tableau Visionary and Zen MasterKey FeaturesLearn Tableau's foundational principles while exploring new AI features like Tableau Pulse and AgentTransform complex datasets into interactive insights with Tableau PrepGain deeper insights using geospatial analysis and advanced methodsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTableau 2025 marks a new era in data visualization and analysis, bringing together advanced AI integrations and dynamic user experiences. This sixth edition, written by Tableau Visionary and Zen Master Joshua Miligan, is an end-to-end guide to mastering the latest innovations in Tableau that transform raw data into actionable insights.This edition introduces groundbreaking features like Tableau AI (including Tableau Pulse and Tableau Agent), enhancing your analytical capabilities with AI-driven data exploration and automated insights. With detailed walkthroughs, you’ll learn to build dynamic dashboards that respond to your data in real time and work with sophisticated AI functionalities that predict trends and model scenarios.Whether you're a seasoned data professional or new to Tableau, this book provides the tools you need to leverage Tableau’s full potential. From integrating diverse data sources using the enhanced data model to employing advanced geospatial functions for detailed mapping, every chapter is packed with expert knowledge and practical applications designed to put powerful analytics at your fingertips.What you will learnImplement advanced AI features to streamline data analysisBuild and customize dynamic dashboards for interactive data storytellingLeverage new geospatial functions for comprehensive mappingEnhance data prep with Tableau Prep's new featuresIntegrate and analyze data from multiple sources effectivelyShare your data stories to build a culture of trust and actionWho this book is forThis Tableau book is for aspiring BI developers and data analysts, data scientists, researchers, and anyone else who wants to gain a deeper understanding of data using Tableau. This book takes you from the ground up, so you won't need any prior experience with Tableau before you dive in, but a full Tableau license (or 14-day demo license) is essential to be able to make use of all the exercises.
R 1,954.00
Data Science and Big Data Computing
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.
R 3,853.32
R 394.00
9 Pitfalls of Data Science
Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the publicdistinguish between good and bad data science.
R 1,895.00
Practical Guide to Data Analysis for Physical Science Students
It is usually straightforward to calculate the result of a practical experiment in the laboratory. Estimating the accuracy of that result is often regarded by students as an obscure and tedious routine, involving much arithmetic. An estimate of the error is, however, an integral part of the presentation of the results of experiments. This textbook is intended for undergraduates who are carrying out laboratory experiments in the physical sciences for the first time. It is a practical guide on how to analyse data and estimate errors. The necessary formulas for performing calculations are given, and the ideas behind them are explained, although this is not a formal text on statistics. Specific examples are worked through step by step in the text. Emphasis is placed on the need to think about whether a calculated error is sensible. At first students should take this book with them to the laboratory, and the format is intended to make this convenient. The book will provide the necessary understanding of what is involved, should inspire confidence in the method of estimating errors, and enable numerical calculations without too much effort. The author's aim is to make practical classes more enjoyable. Students who use this book will be able to complete their calculations quickly and confidently, leaving time to appreciate the basic physical ideas involved in the experiments.
R 2,127.75
Learn Web Development with Python
A comprehensive guide to Python programming for web development using the most popular Python web framework - DjangoKey FeaturesLearn the fundamentals of programming with Python and building web appsBuild web applications from scratch with DjangoCreate real-world RESTful web services with the latest Django frameworkBook DescriptionIf you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.Learn Web Development with Python will get you started with Python programming techniques, show you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. Last but not least, you’ll learn the best practices for creating real-world applications.By the end of this Learning Path, you will have a full understanding of how Django works and how to use it to build web applications from scratch.This Learning Path includes content from the following Packt products:Learn Python Programming by Fabrizio RomanoDjango RESTful Web Services by Gastón C. HillarDjango Design Patterns and Best Practices by Arun RavindranWhat you will learnExplore the fundamentals of Python programming with interactive projectsGrasp essential coding concepts along with the basics of data structures and control flowDevelop RESTful APIs from scratch with Django and the Django REST FrameworkCreate automated tests for RESTful web servicesDebug, test, and profile RESTful web services with Django and the Django REST FrameworkUse Django with other technologies such as Redis and CeleryWho this book is forIf you have little experience in coding or Python and want to learn how to build full-fledged web apps, this Learning Path is for you. No prior experience with RESTful web services, Python, or Django is required, but basic Python programming experience is needed to understand the concepts covered.
R 1,628.00
Learn Computer Science with Swift
Master the basics of solving logic puzzles, and creating algorithms using Swift on Apple platforms. This book is based on the curriculum currently being used in common computer classes. You’ll learn to automate algorithmic processes that scale using Swift in the context of iOS, macOS, tvOS, and watchOS.Begin by understanding how to think computationally: to formulate a computational problem and recognize patterns and ways to validate it. Then jump ahead past the abstractions and conceptual work into using code snippets to build frameworks and write code using Xcode and Swift. Once you have frameworks in place, you’ll learn to use algorithms and structure data. Finally, you’ll see how to bring people into what you’ve built through a useable UI and how UI and code relate.What You'll LearnRecognize patterns and use abstractionsBuild code into reusable frameworksManage code and share version controlSolve logic puzzlesWho This Book Is For Young professionals interested in learning computer science from an Apple platform standpoint.
R 684.00
Machine Learning and Data Science in the Oil and Gas Industry
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.
R 4,267.00
R 541.00
Reliability Analysis Using MINITAB and Python
Reliability Analysis Using MINITAB and Python Complete overview of the theory and fundamentals of Reliability Analysis applied with Minitab and Python tools Reliability Analysis Using Minitab and Python expertly applies Minitab and Python programs to the field of reliability engineering, presenting basic concepts and explaining step-by-step how to implement statistical distributions and reliability analysis methods using the two programming languages. The textbook enables readers to effectively use software to efficiently process massive amounts of data while also reducing human error. Examples and case studies as well as exercises and questions are included throughout to enable a smooth learning experience. Excel files containing the sample data and Minitab and Python example files are also provided. Students who have basic knowledge of probability and statistics will find this textbook highly approachable. Nonetheless, it also covers material on basic statistics at the beginning, so students who are not familiar with statistics can follow the material as well. Written by a highly qualified author in the field, sample topics covered in Reliability Analysis Using Minitab and Python include: Establishing a basic statistical background, with a focus on probability, joint probability, union probability, conditional probability, mutually exclusive events, and bayes’ rule Statistical distributions, with a focus on discrete cases, continuous cases, exponential distribution, Weibull distribution, normal distribution, and lognormal distribution Reliability data plotting, with a focus on straight line properties, least squares fit, linear rectification, exact failure times, and readout failure data Accelerated life testing, with a focus on accelerated testing theory, exponential distribution acceleration, and Weibull distribution acceleration System failure modeling, with a focus on reliability block diagram, series system model, parallel system model, k-out-of-n system model, and minimal paths and minimal cuts. Repairable systems, with a focus on corrective and preventive maintenances, availability, maintainability, and preventive maintenance scheduling Reliability Analysis Using Minitab and Python serves as an excellent introductory level textbook on the topic for both undergraduate and graduate students. It presents information clearly and concisely and includes many helpful additional learning resources to aid in understanding of concepts, information retention, and practical application.
R 3,824.00
Unlocking Data with Generative AI and RAG
Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integrationFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild next-gen AI systems using agent memory, semantic caches, and LangMemImplement graph-based retrieval pipelines with ontologies and vector searchCreate intelligent, self-improving AI agents with agentic memory architecturesBook DescriptionDeveloping AI agents that remember, adapt, and reason over complex knowledge isn’t a distant vision anymore; it’s happening now with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide leads you to the forefront of agentic system design, showing you how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.You’ll master the building blocks of agentic memory, including semantic caches, procedural learning with LangMem, and the emerging CoALA framework for cognitive agents. You’ll also learn how to integrate GraphRAG with tools such as Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops to create systems that continuously learn and refine their behavior. With hands-on code and production-ready patterns, you’ll be ready to build advanced AI systems that not only generate answers but also learn, recall, and evolve.Written by a seasoned AI educator and engineer, this book blends conceptual clarity with practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.*Email sign-up and proof of purchase requiredWhat you will learnArchitect graph-powered RAG agents with ontology-driven knowledge basesBuild semantic caches to improve response speed and reduce hallucinationsCode memory pipelines for working, episodic, semantic, and procedural recallImplement agentic learning using LangMem and prompt optimization strategiesIntegrate retrieval, generation, and consolidation for self-improving agentsDesign caching and memory schemas for scalable, adaptive AI systemsUse Neo4j, LangChain, and vector databases in production-ready RAG pipelinesWho this book is forIf you’re an AI engineer, data scientist, or developer building agent-based AI systems, this book will guide you with its deep coverage of retrieval-augmented generation, memory components, and intelligent prompting. With a basic understanding of Python and LLMs, you’ll be able to make the most of what this book offers.
R 1,628.00
First Course In Probability For Computer And Data Science, A
In this undergraduate text, the author has distilled the core of probabilistic ideas and methods for computer and data science. The book emphasizes probabilistic and computational thinking rather than theorems and proofs. It provides insights and motivates the students by telling them why probability works and how to apply it.The unique features of the book are as follows:This book contains many worked examples. Numerous instructive problems scattered throughout the text are given along with problem-solving strategies. Several of the problems extend previously covered material. Answers to all problems and worked-out solutions to selected problems are also provided.Henk Tijms is the author of several textbooks in the area of applied probability and stochastic optimization. In 2008, he received the prestigious INFORMS Expository Writing Award for his work. He also contributed engaging probability puzzles to The New York Times' former Numberplay column.
R 3,226.06
Data Science and Social Research
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
R 5,402.47
Machine Learning and Modeling Techniques in Financial Data Science
The integration of machine learning and modeling in finance is transforming how data is analyzed, enabling more accurate predictions, risk assessments, and strategic planning. These advanced techniques empower financial professionals to uncover hidden patterns, automate complex processes, and enhance decision-making in volatile markets. As industries increasingly rely on data-driven insights, the adoption of these tools contributes to greater efficiency, reduced uncertainty, and competitive advantage. This technological shift not only drives innovation within financial sectors but also supports broader economic stability and growth by improving forecasting and mitigating risks. Machine Learning and Modeling Techniques in Financial Data Science provides an updated review and highlights recent theoretical advances and breakthroughs in professional practices within financial data science, exploring the strategic roles of machine learning and modeling techniques across various domains in finance. It offers a comprehensive collection that brings together a wealth of knowledge and experience. Covering topics such as algorithmic trading, financial technology (FinTech), and natural language processing (NLP), this book is an excellent resource for business professionals, leaders, policymakers, researchers, academicians, and more.
R 9,710.61
Graduate Students in Data Science Consulting
This manual provides a practical guide on how graduate students can work in statistical and data science consulting and offers valuable resources for anyone interested in creating, expanding, or maintaining statistical or data science consulting programs in higher education across multiple disciplines.Key Features:● Hands on, practical guide to working with graduate students in data science consulting programs.● Broadly applicable across disciplines and program types.● Based on years of experience successfully working with graduate students as data science consultants.This book is aimed at readers looking to set up a consulting program with their graduate students, as well as graduate students looking to consult during their studies.A PDF version of this book is available for free in Open Access at www.taylorfrancis.com. It has been made available under a Creative Commons Attribution- NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 license.
R 3,297.00
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.
R 1,607.23
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
R 611.00
Beyond LLMs: The Next Frontier of AI
Build AI systems that move beyond LLM demos by combining memory, planning, control, and evaluation to create reliable, production-ready intelligenceKey FeaturesDesign AI systems with memory and control instead of relying on prompts aloneMeasure reliability, safety, cost, and drift with evaluation and monitoring patternsBuild and evolve a production-style AI project across the book in PythonBook DescriptionArtificial Intelligence (AI) has moved very quickly in recent years, and large language models (LLMs) have opened the door for teams to build impressive prototypes in a short space of time. However, many of these systems struggle when they need to operate reliably in real-world settings. Prompting alone is often not enough when the goal is consistency, control, observability, and long-term performance. Teams now need to think beyond single model calls and start designing AI as a system made up of multiple working parts.Beyond LLMs provides a practical way to understand and build the next generation of AI systems using Python. This book introduces you to the core ideas that sit beyond basic LLM usage, including memory, reasoning loops, planning, agents, control mechanisms, evaluation, monitoring, and hybrid constraints.Using a recurring SupportOps AI project across the book, you will see how these capabilities fit together in a structured system rather than as isolated techniques. You will learn how to design modular components, measure reliability and drift, handle failure modes, and make safer update decisions as the system evolves.By the end of the book, you will be able to move from prompt-driven demos to more reliable, production-ready AI systems that are easier to test, monitor, and improve.What you will learnUnderstand AI system design beyond single model callsBuild memory and retrieval for continuity and response qualityCreate reasoning and planning loops for reliable task executionLearn how agents use bounded actions and control policiesMeasure reliability, safety, cost, and drift with evaluationHandle failures and update AI systems with safer rolloutsApply hybrid rules and constraints to improve consistencyWho this book is forThis book is for AI engineers, ML engineers, data scientists, software engineers, technical leads, and advanced students who want to move beyond prompt-based LLM prototypes and build reliable, production-ready AI systems. Readers should be comfortable with Python and understand basic machine learning concepts, including training, inference, evaluation, and LLM behavior. Prior experience with agentic AI, world models, neuro-symbolic AI, or production AI architecture is not required.
R 1,628.00
Bayesian Reasoning and Machine Learning
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
R 3,829.00
R 1,400.00
Mathematical Pictures at a Data Science Exhibition
This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.
R 4,067.64
R 1,025.00
Data Science Advancements in Pandemic and Outbreak Management
Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.
R 9,646.08
Handbook of Deep Learning Models
This volume covers a comprehensive range of fundamental concepts in deep learning and artificial neural networks, making it suitable for beginners looking to learn the basics.Using Keras, a popular neural network API in Python, this book offers practical examples that reinforce the theoretical concepts discussed. Real-world case studies add relevance by showing how deep learning is applied across various domains. The book covers topics such as layers, activation functions, optimization algorithms, backpropagation, convolutional neural networks (CNNs), data augmentation, and transfer learning – providing a solid foundation for building effective neural network models.This book is a valuable resource for anyone interested in deep learning and artificial neural networks, offering both theoretical insights and practical implementation experience.
R 10,061.65
Discrete Mathematics for Data Science
Discrete Mathematics for Data Science provides an early course in both Data Science and Discrete Mathematics, focusing on how a deeper understanding of the former can unlock a more effective implementation of the latter. Students of Data Science come from a variety of disciplines, with Business, Statistics, Computer Science, Economics, and Psychology among the departments offering courses on the subject. Therefore, for many students, Data Science is considered a means of insight into a particular field of interest, with the study of its underlying discrete mathematics not a primary objective.This book covers the topics of Discrete Mathematical Structures relevant to students of Data Science, offering a relevant and gentle introduction to both the theoretical and practical elements required to be a successful data scientist. The relaxed, accessible style makes it a perfect textbook for undergraduates.Features• Numerous exercises and examples.• Ideal as a textbook for a Discrete Mathematics course for data science and computer science students.• Source code and solutions provided as a supplementary resource.
R 9,336.27
Trends of Data Science and Applications
This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.
R 5,014.01
Learning Analytics
This book explores how data science, which involves preparing, analyzing, visualizing, and interpreting data, can revolutionize the field of education. The authors delve into how schools and universities can analyze data to improve teaching methods, enhance student learning, and design effective evaluations.Learning Analytics: Shaping the Future of Education with Data Science examines how machine learning algorithms can analyze individual student performance data to tailor personalized adaptive learning paths, ensuring the best educational experience. Through real-world examples, this book discusses how valuable insights and opportunities can be gained through the application of data science in educational environments. The authors discuss the application of natural language processing (NLP) to analyze educational content, providing insights into language usage, comprehension levels, and improving the effectiveness of instructional materials and examines computer vision in classroom dynamics to measure student engagement. The book also exposes the reader to the crucial role of cybersecurity in safeguarding sensitive student and institutional information, ensuring a secure learning environment, and protecting against cyber threats. It also addresses the ethical considerations and privacy concerns associated with collecting, analyzing, and making decisions from educational data. Finally, it emphasizes the importance of responsible practices to protect the rights and well-being of students and educators.The book is intended for engineers from computer science, government policy makers, institutions, and educational stakeholders. It shows how computer science, statistics, and data can personalize learning, improve educational tools, enhance classroom dynamics, secure academic records with blockchain, and ensure online safety.
R 6,118.00
Guide to Teaching Computer Science
This concise yet thorough textbook presents an active-learning model for the teaching of computer science. Offering both a conceptual framework and detailed implementation guidelines, the work is designed to support a Methods of Teaching Computer Science (MTCS) course, but may be applied to the teaching of any area of computer science at any level, from elementary school to university. This text is not limited to any specific curriculum or programming language, but instead suggests various options for lesson and syllabus organization.Fully updated and revised, the third edition features more than 40 new activities, bringing the total to more than 150, together with new chapters on computational thinking, data science, and soft concepts and soft skills. This edition also introduces new conceptual frameworks for teaching such as the MERge model, and new formats for the professional development of computer science educators.Topics and features: includes an extensive set of activities, to further support the pedagogical principles outlined in each chapter; discusses educational approaches to computational thinking, how to address soft concepts and skills in a MTCS course, and the pedagogy of data science (NEW); focuses on teaching methods, lab-based teaching, and research in computer science education, as well as on problem-solving strategies; examines how to recognize and address learners' misconceptions, and the different types of questions teachers can use to vary their teaching methods; provides coverage of assessment, teaching planning, and designing a MTCS course; reviews high school teacher preparation programs, and how prospective teachers can gain experience in teaching computer science.This easy-to-follow textbook and teaching guide will prove invaluable to computer science educators within all frameworks, including university instructors and high school teachers, as well asto instructors of computer science teacher preparation programs.
R 3,663.00
R 1,912.02
Langchain for Life Science and Healthcare
Feeling overwhelmed by the volume of data in your research? Sifting through massive amounts of data to find useful insights is becoming increasingly difficult in drug discovery, genetics, and healthcare. Enter the era of generative AI with LangChain, whose groundbreaking tools are changing the way life scientists and researchers operate.In this groundbreaking book, Dr. Ivan Reznikov teaches you to harness the power of AI to elevate your research capabilities. Divided into two parts, the first is essential for any specialist, covering the transition from traditional statistics to generative AI, the fundamentals of large language models, and the practical uses of LangChain. The second part is designed for life science professionals who want to create AI applications for biology, chemistry, drug development, and more. By the end, you will:Learn how to easily create and integrate LangChain applications into researchDiscover how to substantially accelerate your experimental and data analysis operationsExplore cutting-edge AI solutions designed to address complex research problemsGain the skills and knowledge to advance your career in AI-enhanced life sciences
R 1,824.00
Advanced Snowflake
As Snowflake's capabilities expand, staying updated with its latest features and functionalities can be overwhelming for users. The platform's rapid development introduces advanced tools like Snowpark and the Native App Framework, which are crucial for optimizing data operations but may seem complex to navigate without a structured framework.In this essential book, author Muhammad Fasih Ullah offers a detailed guide to understanding these sophisticated tools, ensuring you can leverage the full potential of Snowflake for data processing, application development, and deploying machine learning models at scale. You'll gain actionable insights and structured examples to transform your understanding and skills in handling advanced data scenarios within Snowflake. By the end of this book, you will:Grasp advanced features such as Snowpark, Snowflake App Framework, and Iceberg tablesEnhance your projects with geospatial functions for comprehensive geospatial analyticsInteract with Snowflake using a variety of programming languages through SnowparkImplement and manage machine learning models effectively using Snowpark MLDevelop and deploy applications within the Snowflake environment
R 1,596.00
Mathematics for Digital Science 3
Over the past century, advancements in computer science have consistently resulted from extensive mathematical work. Even today, innovations in the digital domain continue to be grounded in a strong mathematical foundation. To succeed in this profession, both today's students and tomorrow’s computer engineers need a solid mathematical background. The goal of this book series is to offer a solid foundation of the knowledge essential to working in the digital sector. Across three volumes, it explores fundamental principles, digital information, data analysis, and optimization. Whether the reader is pursuing initial training or looking to deepen their expertise, the Mathematics for Digital Science series revisits familiar concepts, helping them refresh and expand their knowledge while also introducing equally essential, newer topics.
R 5,373.00
R 1,313.00
