Search results for "Health Analytics with Python"
Items we need to order for you
R 1,221.00
R 836.28
R 740.48
Introduction to Python in Earth Science Data Analysis
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
R 2,624.51
R 489.00
Beginning Python Visualization
We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts discusses turning many types of small data sources into useful visual data. And, you will learn Python as part of the bargain.
R 1,570.19
Introduction to Quantitative Social Science with Python
Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.Key Features:Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis.Includes comprehensive quantitative methodologies for quantitative social science studies.Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience.Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.
R 3,007.36
R 3,256.00
Relational Analytics
This guidebook goes beyond people analytics to provide a research-based, practice-tested methodology for doing relational analytics, based on the science of relational coordination. We are witnessing a revolution in people analytics, where data are used to identify and leverage human talent to drive performance outcomes. Today’s workplace is interdependent, however, and individuals drive performance through networks that span department, organization and sector boundaries. This book shares the relational coordination framework, with a validated scalable analytic tool that has been used successfully across dozens of countries and industries to understand, measure and influence networks of relationships in and across organizations, and which can be applied at any level in the private and public sectors worldwide. Graduate students and practitioners in human resource management, health policy and management, organizational behavior, engineering and network analysis will appreciate the methodology and hands-on guidance this book provides, with its focus on identifying, analyzing and building networks of productive interdependence. Online resources include data appendices and statistical commands that can be used to conduct all these analyses in readers’ own organizations.
R 1,905.00
Esri Advanced Guide to Python in ArcGIS
Solving complex geospatial problems and delivering solutions has never been easier.Ready to take your Python knowledge to the next level? Esri Advanced Guide to Python in ArcGIS guides working GIS professionals through integrating Python with ArcGIS capabilities with increasing complexity through each consecutive chapter. Every chapter includes a project that teaches technical skills and problem-solving applicable to practical, job-focused assignments, including understanding client needs, performing market analytics, and managing ArcGIS Online organizations. Esri Advanced Guide to Python in ArcGIS uses step-by-step tutorials with an informal, conversational style of writing so you can easily follow and immediately apply the skills explained. Topics include: Designing effective GIS Python solutions ArcPy and ArcGIS API for Python basics Using imagery and GeoAI for advanced analysis Object-oriented programming Delivering work in various formats: Python scripts, Jupyter Notebook, ArcGIS Pro tools, geoprocessing services, and web applications Data engineering and manipulation techniques Managing ArcGIS Enterprise, ArcGIS Online, and other portal administration Perfect for intermediate to advanced programmers looking to enhance their GIS capabilities, Esri Advanced Guide to Python in ArcGIS will quickly help you become skilled at using Python for geospatial solutions and become more proficient and capable in your work.
R 2,564.00
R 1,404.00
R 550.00
R 916.00
R 813.90
R 1,527.00
Learning Geospatial Analysis with Python
Harness the powerful Python programming language to navigate the realms of geographic information systems, remote sensing, topography, and more, while embracing a guiding framework for effective geospatial analysisKey FeaturesCreate GIS solutions using the new features introduced in Python 3.10Explore a range of GIS tools and libraries, including PostGIS, QGIS, and PROJIdentify the tools and resources that best align with your specific needsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.What you will learnAutomate geospatial analysis workflows using PythonUnderstand the different formats in which geospatial data is availableUnleash geospatial tech tools to create stunning visualizationsCreate thematic maps with Python tools such as PyShp, OGR, and the Python Imaging LibraryBuild a geospatial Python toolbox for analysis and application developmentUnlock remote sensing secrets, detect changes, and process imageryLeverage ChatGPT for solving Python geospatial solutionsApply geospatial analysis to real-time data tracking and storm chasingWho this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.
R 1,368.00
R 1,343.00
Large Language Models for Automatic Deidentification of Sensitive Health Information in Clinical Speech
This volume constitutes the refereed proceedings of the 2025 International Workshop on Deidentification of Electronic Health Record Notes, IW-DMRN 2025, held in Taipei, Taiwan, during August 10, 2025.The 9 full papers were included in this were carefully reviewed and selected from 25 submissions. They focus on the foundational requirement for enabling the safe, scalable, and ethical secondary use of healthcare data. Clinical documentation and patient–clinician communications.
R 3,865.29
Text Analysis in Python for Social Scientists
Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no limit to the kinds of things we can predict from text: power, trust, misogyny, are all signaled in language. These algorithms easily scale to corpus sizes infeasible for manual analysis. Prediction algorithms have become steadily more powerful, especially with the advent of neural network methods. However, applying these techniques usually requires profound programming knowledge and machine learning expertise. As a result, many social scientists do not apply them. This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well as the emerging potential of neural network methods.
R 865.00
R 642.00
R 1,099.00
Data Analysis with Small Samples and Non-Normal Data
In social sciences, education, and public health research, researchers often conduct small pilot studies (or may have planned for a larger sample but lost too many cases due to attrition or missingness), leaving them with a smaller sample than they expected and thus less power for their statistical analyses. Similarly, researchers may find that their data are not normally distributed — especially in clinical samples — or that the data may not meet other assumptionsrequired for parametric analyses. In these situations, nonparametric analytic strategies can be especially useful, though they are likely unfamiliar. A clearly written reference book, Data Analysis with Small Samples and Non-Normal Data offers step-by-step instructions for each analytic technique inthese situations. Researchers can easily find what they need, matching their situation to the case-based scenarios that illustrate the many uses of nonparametric strategies. Unlike most statistics books, this text is written in straightforward language (thereby making it accessible for nonstatisticians) while providing useful information for those already familiar with nonparametric tests. Screenshots of the software and output allow readers to follow along with each step of an analysis.Assumptions for each of the tests, typical situations in which to use each test, and descriptions of how to explain the findings in both statistical and everyday language are all included for each nonparametric strategy. Additionally, a useful companion website provides SPSS syntax for each test, alongwith the data set used for the scenarios in the book. Researchers can use the data set, following the steps in the book, to practice each technique before using it with their own data. Ultimately, the many helpful features of this book make it an ideal long-term reference for researchers to keep in their personal libraries.
R 1,411.09
R 824.00
Secure big-data analytics for emerging healthcare in 5G and beyond
Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs. The editors cover current and emerging frameworks, architectures, and solutions that address the requirements of secure big data analytics for the healthcare industry. The book will cover the challenges of deploying security-based healthcare analytics for massive BDA (big data analytics) applications, through smart optimized network communication infrastructures, dense connectivity, and AI-driven models. Topics include big data analytics, trustworthy data sharing, security challenges and privacy preserving techniques, authentication and access control schemes, deep learning models, risk modelling, and blockchain integration. The book provides a great reference for researchers in academia, network professionals, healthcare industry professionals, and researchers working towards emerging secure BDA solutions in 5G and beyond networks.
R 4,559.00
Bioinformatics Tools and Big Data Analytics for Patient Care
Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks.Features:Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care.Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery.Summarizes several strategies, analyses, and optimization methods for patient healthcare.Focuses on drug discovery and development by cloud computing and data-driven researchThe targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.
R 6,692.97
Applied Data Analytics
The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
R 4,763.00
R 611.00
R 1,254.00
Internet of Things with Python
Interact with the world and rapidly prototype IoT applications using PythonAbout This Book• Rapidly prototype even complex IoT applications with Python and put them to practical use• Enhance your IoT skills with the most up-to-date applicability in the field of wearable tech, smart environments, and home automation• Interact with hardware, sensors, and actuators and control your DIY IoT projects through PythonWho This Book Is ForThe book is ideal for Python developers who want to explore the tools in the Python ecosystem in order to build their own IoT applications and work on IoT-related projects. It is also a very useful resource for developers with experience in other programming languages that want to easily prototype IoT applications with the Intel Galileo Gen 2 board.What You Will Learn• Prototype and develop IoT solutions from scratch with Python as the programming language• Develop IoT projects with Intel Galileo Gen 2 board along with Python• Work with the different components included in the boards using Python and the MRAA library• Interact with sensors, actuators, and shields• Work with UART and local storage• Interact with any electronic device that supports the I2C bus• Allow mobile devices to interact with the board• Work with real-time IoT and cloud services• Understand Big Data and IoT analyticsIn DetailInternet of Things (IoT) is revolutionizing the way devices/things interact with each other. And when you have IoT with Python on your side, you'll be able to build interactive objects and design them.This book lets you stay at the forefront of cutting-edge research on IoT. We'll open up the possibilities using tools that enable you to interact with the world, such as Intel Galileo Gen 2, sensors, and other hardware. You will learn how to read, write, and convert digital values to generate analog output by programming Pulse Width Modulation (PWM) in Python. You will get familiar with the complex communication system included in the board, so you can interact with any shield, actuator, or sensor.Later on, you will not only see how to work with data received from the sensors, but also perform actions by sending them to a specific shield. You'll be able to connect your IoT device to the entire world, by integrating WiFi, Bluetooth, and Internet settings. With everything ready, you will see how to work in real time on your IoT device using the MQTT protocol in python.By the end of the book, you will be able to develop IoT prototypes with Python, libraries, and tools.Style and approach This book takes a tutorial-like approach with mission critical chapters. The initial chapters are introductions that set the premise for useful examples covered in later chapters.
R 1,889.00
R 1,191.00
R 439.00
R 275.00
Handbook of Graphs and Networks in People Analytics
Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form.The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level.Key features:Immediately implementable code, with extensive and varied illustrations of graph variants and layoutsExamples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigationDedicated chapter on graph visualization methodsPractical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachmentVarious downloadable data sets for use both in class and individual learning projectsFinal chapter dedicated to individual or group project examples
R 11,704.73
R 580.00
Advancing Big Data Analytics for Healthcare Service Delivery
In recent years, there has been steady increase in the interest shown in both big data analytics and the use of information technology (IT) solutions to improve healthcare services. Despite the growing interest, there are limited materials, to addressing the needs and challenges posed by the activities and processes including the use of big data. From IT solutions’ perspectives, this book aims to advance the deployment and use of big data analytics to increase patients’ big data usefulness and improve healthcare service delivery.The book provides significant insights and useful guide on how to access and manage big data, in improving healthcare service delivery. The book contributes a fresh perspective, which primarily comes from the complementary use of analytics approach with actor-network theory (ANT), and other techniques, in advancing healthcare service delivery. Accessing and managing healthcare big data have always been a challenging exercise. Due to the sensitivity of the health sector, the focus on patients’ big data is from either technical or social perspective. Thus, the book employs sociotechnical theories, ANT and structuration theory (ST) as lenses to examine and explain the factors that enable and constrain the use of patients’ big data for health services. By doing so, the book brings a different dimension and advance health service delivery.Providing a timely and important contribution to this critical area, this book is a valuable, international resource for academics, postgraduate students and researchers in the areas of IT, big data analytics, data management and health informatics.
R 2,431.42
Stochastic Modeling And Analytics In Healthcare Delivery Systems
In recent years, there has been an increased interest in the field of healthcare delivery systems. Scientists and practitioners are constantly searching for ways to improve the safety, quality and efficiency of these systems in order to achieve better patient outcome.This book focuses on the research and best practices in healthcare engineering and technology assessment. With contributions from researchers in the fields of healthcare system stochastic modeling, simulation, optimization and management, this is a valuable read.
R 5,072.65
R 671.00
Python for Information Professionals
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science. Readers of Python for Information Professionals will learn to:Develop Python applications for the retrieval, cleaning, and analysis of large datasets. Design applications to support traditional library functions and create new opportunities to maximize library value. Consider data security and privacy relevant to data analysis when using the Python language.
R 4,030.00
Application of Intelligent Systems in Multi-modal Information Analytics
This book provides comprehensive coverage of the latest advances and trends in information technology, science and engineering. Specifically, it addresses a number of broad themes, including multi-modal informatics, data mining, agent-based and multi-agent systems for health and education informatics, which inspire the development of intelligent information technologies. The book covers a wide range of topics such as AI applications and innovations in health and education informatics; data and knowledge management; multi-modal application management; and web/social media mining for multi-modal informatics. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and a useful reference guide for newcomers to the field. This book is a compilation of the papers presented in the 4th International Conference on Multi-modal Information Analytics, held online, on April 23, 2022.
R 6,228.00
R 672.00
R 611.00
Computational Frameworks for Political and Social Research with Python
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
R 4,260.30
Geospatial Data Analytics on AWS
Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook.Key FeaturesExplore the architecture and different use cases to build and manage geospatial data lakes in AWSDiscover how to leverage AWS purpose-built databases to store and analyze geospatial dataLearn how to recognize which anti-patterns to avoid when managing geospatial data in the cloudBook DescriptionManaging geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.What you will learnDiscover how to optimize the cloud to store your geospatial dataExplore management strategies for your data repository using AWS Single Sign-On and IAMCreate effective SQL queries against your geospatial data using AthenaValidate postal addresses using Amazon Location servicesProcess structured and unstructured geospatial data efficiently using RUse Amazon SageMaker to enable machine learning features in your applicationExplore the free and subscription satellite imagery data available for use in your GISWho this book is forIf you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.
R 1,596.00
R 592.00
R 1,527.00
R 1,220.00
