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Art of Data Science
Although change is constant in business and analytics, some fundamental principles and lessons learned are truly timeless, extending and surviving beyond the rapid ongoing evolution of tools, techniques, and technologies. Through a series of articles published over the course of his 30+ year career in analytics and technology, Doug Gray shares the most important lessons he has learned – with colleagues and students as well – that have helped to ensure success on his journey as a practitioner, leader, and educator.The reader witnesses the Analytical Sciences profession through the mind’s eye of a practitioner who has operated at the forefront of analytically inclined organizations, such as American Airlines and Walmart, delivering solutions that generate hundreds of millions of dollars annually in business value, and an educator teaching students and conducting research at a leading university. Through real‑world project case studies, first‑hand stories, and practical examples, we learn the foundational truth underlying successful analytics applications. From bridging theory and practice, to playing a role as a consultant in digital transformation, to understanding how analytics can be economically transformational, identifying required soft skills like leadership skills, and understanding the reasons why data science projects often fail, the reader can better visualize and understand the nuanced, multidimensional nature of Analytical Sciences best practices, projects, and initiatives.The readers will gain a broad perspective on where and how to find success with Analytical Sciences, including the ability to ensure that we apply the right tool, at the right time and right place, and sometimes in different industries.Finally, through the author’s own career synopsis on becoming a practitioner and leader, and his distilled insights, the reader is offered a view into the future that analytics holds, along with some invaluable career advice regarding where to focus, how to make good choices, and how to measure success individually and organizationally.
R 2,945.95
Student Guide to AI and Robotics
This book provides a practical and accessible introduction to AI and robotics, presenting the foundational technologies driving the development of intelligent systems. It covers key topics such as machine learning, perception, control, and autonomous decision-making, balancing theoretical concepts with practical applications.Through selected examples and case studies, the book explores robotics platforms and applied AI technologies used across industries, offering insights into their transformative potential in real-world scenarios. With a focus on hands-on learning, it introduces programming languages and tools commonly used in AI and robotics, empowering readers to apply concepts and techniques effectively. It also addresses the importance of building secure, responsible systems and examines the future of human-AI collaboration.An essential resource for introductory AI and robotics courses, this book provides the foundational knowledge needed to advance in these dynamic fields.
R 2,674.00
Data Science for Healthcare
This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare.Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising.This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.
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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.
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R for Data Science Cookbook
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesAbout This Book• Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages• Understand how to apply useful data analysis techniques in R for real-world applications• An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisWho This Book Is ForThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.What You Will Learn• Get to know the functional characteristics of R language• Extract, transform, and load data from heterogeneous sources• Understand how easily R can confront probability and statistics problems• Get simple R instructions to quickly organize and manipulate large datasets• Create professional data visualizations and interactive reports• Predict user purchase behavior by adopting a classification approach• Implement data mining techniques to discover items that are frequently purchased together• Group similar text documents by using various clustering methodsIn DetailThis cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.Style and approachThis easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.
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Rag with Python Cookbook
As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.Learn core RAG components including embedding, retrieval, and generation techniquesUnderstand advanced workflows like semantic-aware chunking and multi-query promptingBuild custom solutions such as chatbots and autonomous agents for specific data challengesContinuously evaluate and optimize systems for accuracy, relevance, and performance
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Data Science
As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practice is packed with up-to-date examples and data, ensuring it’s both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter, is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts towards sustainable development, empowering readers to enhance their decision-making abilities, and discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.
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Nature Inspired Computing for Data Science
This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
R 4,030.00
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.
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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.
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Simulation for Data Science with R
Harness actionable insights from your data with computational statistics and simulations using RAbout This Book• Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies• A unique book that teaches you the essential and fundamental concepts in statistical modeling and simulationWho This Book Is ForThis book is for users who are familiar with computational methods. If you want to learn about the advanced features of R, including the computer-intense Monte-Carlo methods as well as computational tools for statistical simulation, then this book is for you. Good knowledge of R programming is assumed/required.What You Will Learn• The book aims to explore advanced R features to simulate data to extract insights from your data.• Get to know the advanced features of R including high-performance computing and advanced data manipulation• See random number simulation used to simulate distributions, data sets, and populations• Simulate close-to-reality populations as the basis for agent-based micro-, model- and design-based simulations• Applications to design statistical solutions with R for solving scientific and real world problems• Comprehensive coverage of several R statistical packages like boot, simPop, VIM, data.table, dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many more.In DetailData Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world.The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results.By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.Style and approachThis book takes a practical, hands-on approach to explain the statistical computing methods, gives advice on the usage of these
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Data Science for Teams
Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.
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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.
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Practical Data Science
Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets.The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions.What You'll LearnBecome fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteriaMaster the methods for retrieving actionable business knowledgeCoordinate the handling of polyglot data types in a data lake for repeatable resultsWho This Book Is ForData scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers
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Quantitative Social Science Data with R
Relevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs.Gradually introducing applied statistics and the language and functionality of R and R Studio software, it uses examples from across the social sciences to show students how to apply abstract statistical and methodological principles to their own work. Maintaining a student-friendly pace, it goes beyond a normal introductory statistics book and shows students where data originates and how to:- Understand and use quantitative data to answer questions- Approach surrounding ethical issues- Collect quantitative data- Manage, write about, and share the data effectivelySupported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives students not only the tools they need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what they have learned.
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Emerging Trends, Techniques, and Applications in Geospatial Data Science
With the emergence of smart technology and automated systems in today's world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.
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Mobility Data Science
This textbook covers the key topics in mobility data analysis, including all steps of the data science pipeline illustrated with real-world examples. The book is composed of three parts. Part I “Fundamental Concepts” provides the background for this book by introducing spatial and temporal databases and motivating the need for mobility databases. Further chapters in this part are devoted to a formal model for representing mobility data, an introduction to mobility data visualization, and the topic of querying mobility databases. Part II “Advanced Topics” covers topics such as query processing and indexing, illustrated with PostgreSQL, introduces mobility data warehouses using synthetic data, and concludes with distributed mobility databases. Part III “Mobility Analytics” covers important topics like mobility data cleaning, including the identification of erroneous data, and mobility analysis using foundational algorithms for spatial and mobility data. It also includes an urban mobility use case that illustrates the concepts presented throughout the book in a real application setting.This textbook is written for undergraduate and graduate computer science courses on mobility data science. As such, it follows a pedagogical style to make the work of the instructor easier and to help students to understand the concepts being delivered, complementing the presentation with exercises and a companion GitHub repository. SQL is used as a high-level language for analytics, allowing students to write complex data science code, while abstracting away implementation details. Researchers and practitioners who are interested in an introduction to the area of mobility data science will also find the book a useful reference.
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Data Science
This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023.The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections:Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis.Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.
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R 716.00
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Supervised Learning
This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the output and input variables, where said learning is undertaken within the remit of real-world information that can be messy in different ways. For example, the available data may be highly multivariate or be high-dimensional, reflecting the nature of the output variable that could be a vector, or matrix, or even higher in dimension, as is often the case in a real-world application. Additionally, the data is noisy, and often it is small to moderately large in size in multiple applications. Another difficulty that regularly arises is that the training dataset – comprising pairs of values of the input and output –is such, that the sought function cannot be captured by a parametric shape, but is instead underlined by an inhomogeneous correlation structure. These difficulties notwithstanding, we desire a streamlined methodology that allows the learning of the inter variable relationship – to ultimately permit fast and reliable predictions of the output, at newly recorded values of the input. In fact, occasions arise when one seeks values of the input at which a new output value is recorded, and such a demand is also addressed in the book.The generic solution to the problem of secured supervised learning amidst real-world messiness, lies in treating the sought inter-variable relation as a (function-valued) random variable, which, being random, is ascribed a probability distribution. Then recalling that distributions on the space of functions are given by stochastic processes, the sought function is proposed to be a sample function of a stochastic process. This process is chosen as one that imposes minimal constraints on the sought function – identified as a Gaussian Process (GP) in the book. Thus, the sought function can be inferred upon, as long as the co-variance function of the underlying GP is learnt, given the available training set. The book presents probabilistic techniques to undertake said learning, within the challenges borne by the data, and illustrates such techniques on real data. Learning of a function is always followed by closed-form prediction of the mean and dispersion of the output variable that is realised at a test input. To help with the background, the book includes reviews on stochastic processes and basic probability theory. This will render the first half of the book useful for students across disciplines, while the latter half will be appreciated by students of numerate subjects at the postgraduate level or higher, including students of computational sciences, statistics and mathematics.
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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.
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Quantitative Trading Strategies Using Python
Build and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python.Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part two introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part three covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach. Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you’ll come away from it with a firm understanding of core trading strategies and how to use Python to implement them.What You Will LearnMaster the fundamental concepts of quantitative tradingUse Python and its popular libraries to build trading models and strategies from scratchPerform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using PythonUtilize common trading strategies such as trend-following, momentum trading, and pairs tradingEvaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtestingWho This Book Is ForAspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields.
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R 840.00
Silicon Valley Python Engineer Interview Guide
Silicon Valley Python Interview Guide: Data Structures, Algorithms, and System Design is an essential resource for aspiring software engineers preparing for technical interviews at top-tier companies. This book provides a comprehensive roadmap, covering foundational concepts, practical coding techniques, and advanced problem-solving strategies to help candidates excel in interviews. With a focus on Python, the book equips readers with the skills to tackle challenging coding problems, design scalable systems, and communicate solutions effectively. In the first half, the book delves into core data structures (lists, stacks, queues, graphs, and trees) and algorithms (binary search, dynamic programming, DFS, BFS, and backtracking),offering practical examples and Python implementations. The latter half transitions to system design, including big data architectures, distributed systems, and machine learning workflows. Case studies on real-world applications like Tiny URL, autocomplete systems, and Chat GPT-like models provide hands-on insights. Whether you are an early-career engineer or an experienced professional, this guide is designed to enhance your preparation with real-world examples, tested code, and proven strategies. It is more than a technical handbook—it is your roadmap to building confidence and securing a role in the competitive tech industry.
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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.
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R 184.00
Data Science
This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.
R 4,311.44
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.
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