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R 580.00
Mastering Health Data Science Using R
This book provides a practical, application-driven guide to using R for public health and health data science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to apply R skills to their own research needs. Practical case studies in public health are provided throughout to reinforce learning.Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an introduction to probability, statistical inference and regression analysis.Key features:· Includes practical case studies.· Explains how to write larger programmes.· Contains additional information on Quarto.
R 7,592.91
R 367.00
Research in Data Science
This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia.
R 3,430.76
R 519.00
R 1,665.85
R 1,303.00
R 580.00
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
Higher Education Computer Science
The march towards on-line and blended teaching—present before the Covid-19 pandemic—has been accelerated by it, and there is no going back. Students and staff may object, but the economic drive towards “greater productivity” will inevitably result in less face-to-face (f2f) instruction. Therefore, it is incumbent for those delivering this precious, in-person resource to make maximum use of time…which raises the question, “how”?The second edition of Higher Education Computer Science offers some potential answers. It also addresses other questions, such as “why have f2f teaching at all?” “what is the purpose of f2f?” and “what is the appropriate balance between the two?” The first edition began to offer suggestions for optimising limited opportunities to get together with students. Aligned with that, this unique new volume examines how to use the technology available to maximum advantage: For example, resources such as Moocs and other on-line instructional materials can provide invaluable pedagogic support. In addition, the book addresses ‘problem-based learning,’ using robotics in the teaching of programming, and a multidisciplinary approach to data science. Although it includes a chapter on distance learning, there is greater emphasis placed on the soft, transferable skills and employability skills that are best delivered in person. Further, the work provides several examples of putting theory into practice when teaching computer science at both undergraduate and postgraduate levels. Written by experienced practitioners, each chapter tackles a particular teaching activity or topic within computing, presented in such a way that other practitioners can use. As such, this new volume will be an invaluable resource to those who want to protect and optimise in-person teaching.
R 2,362.29
Applied Data Science
The use of data to guide action is growing. Even the public uses data to guide everyday decisions! How do we develop data acumen across a broad range of fields and varying levels of expertise? How do we foster the development of effective data translators? This book explores these questions, presenting an interdisciplinary collection of edited contributions across fields such as education, health sciences, natural sciences, politics, economics, business and management studies, social sciences, and humanities. Authors illustrate how to use data within a discipline, including visualization and analysis, translating and communicating results, and pedagogical considerations. This book is of interest to scholars and anyone looking to understand the use of data science across disciplines. It is ideal in a course for non-data science majors exploring how data translation occurs in various contexts and for professionals looking to engage in roles requiring data translation.
R 8,123.41
Mechanistic Data Science for STEM Education and Applications
This book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems. Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry level textbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.
R 2,198.00
R 1,099.00
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
Deep Learning with C++
Build and deploy high-performance deep learning models using C++ for real-time applications where speed and efficiency matter.Key FeaturesImplement neural networks using the PyTorch C++ API and Caffe2Optimize and deploy deep learning models for real-time inferenceLearn CUDA acceleration, model compression, and monitoring best practicesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDeep Learning with C++ is a hands-on guide to building, optimizing, and deploying deep learning models using the power of C++. Designed for ML engineers, data scientists, and developers working in performance-critical domains, this book provides step-by-step instruction for implementing everything from basic neural networks to CNNs, RNNs, GANs, and LLMs using the PyTorch C++ API, Caffe2, and CUDA.You will begin by setting up a C++ deep learning environment and understanding foundational neural network concepts. Then, you'll move on to building various deep learning architectures, optimizing them for speed, and deploying them with robust monitoring and explainability features. Whether you work in finance, gaming, healthcare, or embedded systems, this book equips you to deploy deep learning systems at scale.Complete with real-world case studies and advanced topics like distributed training, model compression, and explainability, this book ensures you're ready for production-ready AI systems that are fast, scalable, and efficient.What you will learnSet up and use PyTorch C++ API and Caffe2 for deep learningImplement CNNs, RNNs, LSTMs, GANs, and LLMs in C++Leverage CUDA for high-performance model trainingOptimize models through quantization, pruning, and compressionDeploy and monitor models in production using C++ toolsApply explainability techniques like LIME, SHAP, and Grad-CAMWho this book is forThis book is for ML engineers, deep learning practitioners, and data scientists with a solid C++ background who want to build high-performance deep learning models. It also serves developers transitioning from Python-based frameworks looking for real-time deployment solutions in industries like finance, autonomous systems, and healthcare.
R 1,628.00
Data Science and Machine Learning
This book constitutes the proceedings of the 22nd Australasian Conference on Data Science and Machine Learning, AusDM 2024, held in Melbourne, Victoria, Australia, during November 25–27, 2024.The 19 full papers included in this book were carefully reviewed and selected from 47 submissions. They were organized in two topical tracks: Research Track; and Application Track.
R 7,708.35
How to Think about Data Science
This book is a timely and critical introduction for those interested in what data science is (and isn’t), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist’s approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist’s approach to explaining data science through questions and examples.
R 2,308.00
Cloud Computing And Big Data Technology
This book comprehensively introduces the basic knowledge and main technologies of cloud computing and big data. It covers cloud computing, big data technology, virtualization technology, data centers, parallel computing and clustering technology, cloud storage technology, OpenStack, Hadoop, Spark, Storm, and cloud computing simulation.With a focus on practicality, this volume is enriched with experiments and closely integrates theory with practice, enabling readers to systematically and comprehensively understand cloud computing and big data technologies.This useful reference textbook benefits professionals, academics, researchers, graduate and undergraduate students in databases/information sciences.
R 3,965.47
R 396.00
Python Scripting for Computational Science
With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.
R 2,172.77
Deep Learning in Practice
Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures.Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE.This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.
R 1,918.64
Engineering Data Analytics
Engineering Data Analytics introduces students to foundational concepts within the discipline, the centrality of models in analysis methodologies, and the significance of probability in dealing with uncertain quantities. The textbook provides engineering students with the skillsets necessary to evaluate complex systems—whether physical or operational—when closed form or simulation approaches are unavailable or inadequate, as is often the case.The book sheds light on the complex tapestry of engineering data analytics, covering statistical quality control to experimental design strategies. It offers a practical approach by including Python code for implementing various analytical models, illustrating the intersection of theoretical understanding with practical application. Key topics such as probability mass functions, cumulative distribution functions, and the interpretation of ANOVA using the concept of sample variance are given due attention to ensure a comprehensive coverage of the subject matter.Engineering Data Analytics is designed to support coursework at the undergraduate level and is suitable for students who are pursuing degrees in engineering disciplines that necessitate a solid grasp of data analytics principles. It can also serve as a fundamental resource for graduate-level studies, where a more profound dive into the mechanisms and advanced applications of engineering data analytics is required.
R 3,607.63
Data Strategy for LLMs
Learn the essential data techniques for building real-world LLM applications with this practical guide for technical professionals and advanced students, starting from data fundamentals to LLM-specific methods and advanced topics.Key FeaturesLearn state-of-the-art data strategies and techniques for LLM application developmentUnderstand the fundamentals of data collection, processing and synthetic data generationUtilize LLM-specific data techniques for RAG, finetuning and alignment to enhance your LLM applicationGet exposed to multi-modality, pre-training, feedback loops, explainability and more advanced topicsBook DescriptionMost teams building applications with large language models (LLMs) start by choosing a model. Yet the same models are available to everyone, and the one that leads today may be replaced within months. What makes an LLM application reliable is the data around the model: the sources it draws on, the context it receives, the examples it learns from, and the tests that show whether it works.This book walks you through that data lifecycle, from selecting and preparing sources to building a retrieval-augmented generation (RAG) system and fixing the data problems it meets in production. You will apply synthetic data generation to fill coverage gaps, learn what LLM fine-tuning and post-training require, and combine prompts, retrieval, tools, and post-training into one customization process. Examples from four assistant scenarios (personal productivity, domain-specific, enterprise knowledge, and content moderation) show how the same data decision plays out differently in each.You will also create task-specific LLM evaluation data, catch quality problems with automated checks and human oversight, and manage the context that AI agents rely on at runtime. By the end of this book, you will know how to select, prepare, test, and maintain the data that makes LLM applications useful and reliable, with hands-on Python examples you can run and adapt.What you will learnSelect data sources and prepare them as vector-ready knowledgeBuild a RAG system and fix the data problems it hits in productionApply synthetic data generation to fill coverage gapsIdentify the data that LLM fine-tuning, post-training, and alignment requireCustomize models by combining prompts, retrieval, tools, and post-trainingCreate task-specific LLM evaluation datasets and score answers with LLM-as-a-judgeCatch data quality problems with automated checks and human oversightEngineer the context AI agents work from, using the Agentic Repo approachWho this book is forThis book is for AI engineers, scientists, product managers, advanced students and business domain experts looking to deepen their understanding of LLMs and the data techniques for building LLM applications. Basic knowledge of LLMs and the Generative AI landscape is recommended. Whether you are new to the AI space or looking to enhance your AI building skills, this book provides comprehensive guidance on essential d
R 1,791.00
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
Architecture Handbook for Milvus Vector Database
Co-authored by core contributors of Milvus, this book guide explores the architecture of the Milvus vector databases for GenAI solutionsFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesUnderstand the core architecture and vector indexing engine that makes Milvus ideal for AI-driven searchLearn scalable deployment and performance optimization techniquesTest, apply, and integrate Milvus into AI and LLM pipelines using LangChainPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe rapid adoption of LLMs demands efficient storage and lightning-fast retrieval of unstructured data. Designed as a vector database, Milvus has earned widespread recognition in the community and support from tech giants like Apple and NVIDIA. Yet, many developers only scratch the surface of what Milvus is truly capable of. Written by the contributors of the Milvus project, this handbook gives you an insider’s perspective on its design and how it handles large-scale, high-dimensional vector data.Starting with the basics, you’ll learn about everything from service deployment and SDK usage to Milvus’ layered architecture and how its components interact. You’ll learn how the indexing, replication, compaction, and garbage collection systems work and how to apply them to real scenarios. Through practical demos and configuration exercises, you’ll learn how to monitor, scale, and secure Milvus in production and then advance to performance evaluation and scalability testing using tools like VectorDBBench. You'll also explore Milvus' integration with LangChain for use cases such as vector search and RAG-based chatbots.By the end of this book, you’ll be able to analyze Milvus internals, fine-tune for performance, ensure system stability, and integrate it into next-generation AI solutions.*Email sign-up and proof of purchase requiredWhat you will learnDeploy Milvus using Docker, Kubernetes, and HelmConfigure Milvus and monitor system health with Prometheus, Grafana, and LokiUnderstand core components like Knowhere, indexes, time sync, compaction, and garbage collectionDesign and optimize schema, queries, and data modification flowsBenchmark performance and simulate real-world failure recoveryScale Milvus clusters to support large datasets and high-concurrency trafficImplement different multi-tenant strategies in MilvusBuild AI applications using Milvus with LangChainWho this book is forThis book is for database practitioners looking to get started with Milvus and build their expertise in vector data and vector search. It’s particularly suited for data analysts, data scientists, Milvus developers, system architects, tech enthusiasts, and researchers in vector database technologies.To get the most out of this book, you should have a foundational understanding of Go, Python, or C++, as well as a basic knowledge of database systems. Familiarity with Docker and Kubernetes is recommended.
R 1,628.00
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
Image Processing and Acquisition using Python
Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples.A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The second part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples, detailed derivations, and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test the reader’s skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book’s web page.New to this edition Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules.
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R 1,221.00
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
Data Science in Engineering and Management
This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis.Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively.This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.
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R 3,114.00
Data Science for Water Utilities
This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience.Data Science for Water Utilities: Data as a Source of Value is an applied, practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. The last case study analyses data using basic machine learning.Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R.
R 9,964.77
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 4,352.75
R 4 Data Science Quick Reference
In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub.. What You'll LearnImplement applicable R 4 programming language specification featuresImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelrWho This Book Is ForProgrammers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.
R 1,021.49
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
Data Science Using Oracle Data Miner and Oracle R Enterprise
Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.What you'll learnDiscover the functionality of Oracle Data Miner and Oracle R EnterpriseGain methods to perform in-database predictive analyticsUse Oracle's SQL and PLSQL APIs for building analytical solutionsAcquire knowledge of common and widely-used business statistical analysis techniquesWho this book is forIT executives, BI architects, Oracle architects and developers, R users and statisticians.
R 1,424.02
Data Science for COVID-19
Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.
R 6,768.00
R 1,977.25
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 1,985.19
