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R 1,984.00
Healthcare Analytics Made Simple
Add a touch of data analytics to your healthcare systems and get insightful outcomesKey FeaturesPerform healthcare analytics with Python and SQLBuild predictive models on real healthcare data with pandas and scikit-learnUse analytics to improve healthcare performanceBook DescriptionIn recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes.This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed.By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.What you will learnGain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processesUse SQL and Python to analyze dataMeasure healthcare quality and provider performanceIdentify features and attributes to build successful healthcare models Build predictive models using real-world healthcare dataBecome an expert in predictive modeling with structured clinical dataSee what lies ahead for healthcare analyticsWho this book is forHealthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.
R 1,498.00
Practical Healthcare Statistics with Examples in Python and R
Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis.The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data.These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.
R 9,310.57
Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.
R 17,477.68
Sequential Decision Analytics and Modeling: Modeling with Python
Sequential decision problems arise in virtually every human process. They span finance, energy, transportation, health, e-commerce, and supply chains and include pure learning problems that arise in laboratory or field experiments. They even cover search algorithms to maximize uncertain functions. An important dimension of every problem setting is the need to make decisions in the presence of different forms of uncertainty and evolving information processes. Warren B. Powell’s work in sequential decision problems started in the 1980s and spanned rail, energy, health, finance, e-commerce, supply chain management, and even learning for materials science. His work on a wide range of problems highlighted the importance of using a variety of methods. In the process, he came to realize that any sequential decision problem can be modeled using a single universal framework that involves searching over methods for making decisions.The goal of this book is to enable readers to understand how to approach, model and solve a sequential decision problem. To that end, it uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. It tackles the challenge of designing methods, called policies, for making decisions and describes four classes of policies that are universal in that they span any method that might be used; whether from the academic literature or heuristics used in practice. While this does not mean that every problem can be solved immediately, the framework helps avoid the tendency in the academic literature of focusing on narrow classes of methods.
R 3,224.00
Practical Healthcare Statistics with Examples in Python and R
Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis.The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data.These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.
R 3,582.28
Foundations for Analytics with Python
If you're like many of Excel's 750 million users, you want to do more with your data-like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats-by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax Use Python's csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments
R 1,557.00
Health Analytics
A hands-on, analytics road map for health industry leaders The industry-wide transformation taking place across the health and life sciences ecosystem is mandating that organizations adopt new decision-making capabilities, based on science and real-world information. Analytics will be a required competency for the modern health enterprise; this book is about how to "cross the chasm." The ultimate analytics guide for the health industry leader, this essential book equips business leaders with little-to-no experience in analytics to understand how to incorporate analytics as a cornerstone of their 21st century competitive business strategy. Paints the picture for a new health enterprise, one focused on the patientExplores the financial components of this new operating model, using analytics to optimize the tradeoffs between cost and valueDeals with the rising role of the consumer, using analytics to create a completely new health engagement model with individual recipients of careLooks at how analytics can drive innovations in care practice, patient-experienced medical outcomes, and analytically driven novel therapies optimized for the individual patientPresents a variety of text, tables, and graphics illustrating the various concepts being described Within each section and chapter, Health Analytics assesses the current landscape, proposing a new model/concept, sharing real-world stories of how the old and new world come together, and framing a "how-to" for the reader in terms of growing that particular set of capabilities in their own enterprises.
R 1,436.00
Machine Learning for Healthcare Analytics Projects
Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learnKey FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machine learning algorithms using a range of libraries from the Python ecosystemAccomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologiesBook DescriptionMachine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics.This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks.By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.What you will learnExplore super imaging and natural language processing (NLP) to classify DNA sequencingDetect cancer based on the cell information provided to the SVMApply supervised learning techniques to diagnose autism spectrum disorder (ASD)Implement a deep learning grid and deep neural networks for detecting diabetesAnalyze data from blood pressure, heart rate, and cholesterol level tests using neural networksUse ML algorithms to detect autistic disordersWho this book is forMachine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.
R 879.00
Health Analytics with R
This textbook teaches health analytics using examples from the statistical programming language R. It utilizes real-world examples with publicly available datasets from healthcare and direct-to-consumer genetics to provide learners with real-world examples and enable them to get their hands on actual data. This textbook is designed to accompany either a senior-level undergraduate course or a Masters level graduate course on health analytics.The reader will advance from no prior knowledge of R to being well versed in applications within R that apply to data science and health analytics.“I have never seen a book like this and think it will make an important contribution to the field. I really like that it covers environmental, social, and geospatial data. I also really like the coverage of ethics. These aspects of health analytics are often overlooked or deemphasized. I will definitely buy copies for my team.”- Jason Moore, Cedars-Sinai Medical Center“Overall, I have a highly positive impression of the book. It is VERY comprehensive. It covers very extensive data types. I do not recall other books with the same level of comprehensiveness.”- Shuangge Ma, Yale University“The book is comprehensive in both aspects of genetics, and health analytics. It covers any type of information a healthcare data scientist should be familiar with, whether they are novice or experienced. I found any chapter that I looked into comprehensive, but also not too detailed (although in general this book is more than 600 pages of comprehensive and detailed relevant information).”- Robert Moskovtich, Ben-Gurion University of the Negev
R 4,030.00
R 611.00
R 611.00
Mastering Python Data Visualization
Generate effective results in a variety of visually appealing charts using the plotting packages in PythonAbout This Book• Explore various tools and their strengths while building meaningful representations that can make it easier to understand data• Packed with computational methods and algorithms in diverse fields of science• Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuseWho This Book Is ForIf you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed.What You Will Learn• Gather, cleanse, access, and map data to a visual framework• Recognize which visualization method is applicable and learn best practices for data visualization• Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception• Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it• Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics• Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning• Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js• Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environmentIn DetailPython has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences.This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical
R 1,889.00
Healthcare Analytics
This is a comprehensive, practical guide which looks at the advantages and limitations of new data analysis techniques being introduced across public health and administration services.The Affordable Care Act (ACT) and free market reforms in healthcare are generating a rapid change of pace. The "electronification" of medical records from paper to digital, which is required to meet the meaningful use standards set forth by the Act, is advancing what and how information can be analyzed. Coupled with the advent of more computing power and big data analytics and techniques, practitioners now more than ever need to stay on top of these trends. This book presents a comprehensive look at healthcare analytics from population data to geospatial analysis using current case studies and data analysis examples in health. This resource will appeal to undergraduate and graduate students in health administration and public health. It will benefit healthcare professionals and administrators in nursing and public health, as well as medical students who are interested in the future of data within healthcare.
R 1,905.00
Python Data Analytics
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystemGo in depth with pandas for reading, writing, and processing dataUse tools and techniques for data visualization and image analysisExamine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWho This Book Is ForExperienced Python developers who need to learn about Pythonic tools for data analysis
R 1,368.00
R 2,762.00
Hands-On Predictive Analytics with Python
Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook DescriptionPredictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is forThis book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.
R 1,661.00
R 1,047.00
R 611.00
R 611.00
R 947.00
Python 3 and Data Analytics
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, and data cleaning. The book includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regular expressions. Companion files with source code and color figures are available.FEATURES:Includes a concise introduction to Python 3.Provides a thorough introduction to data and data cleaning.Covers NumPy and Pandas.Introduces statistical concepts and data visualization (Matplotlib/Seaborn).Features an appendix on regular expressions.Includes companion files with source code and figures.
R 911.00
R 611.00
Business Analytics with Python
Your essential textbook for mastering business analytics through Python.Business Analytics with Python by Bowei Chen and Gerhard Kling is the definitive guide for upper-level undergraduate and postgraduate students studying business, management or finance. Designed to support analytics modules that prioritize practical application, this textbook introduces students to data-driven decision-making through Python, without assuming a background in computer science. It aligns with course outcomes by integrating statistical, mathematical and machine learning techniques into a unified business context. This textbook takes a holistic approach to business analytics, exploring how Python can be used to interpret and solve real-world problems. From foundational coding skills to the implementation of supervised and unsupervised machine learning methods, students learn how to translate data into insight across key business functions. Through industry-relevant case studies, including customer churn analysis, fraud detection and sales forecasting, learners build confidence in applying analytics to real organizational challenges. Pedagogical features include: - A running case study that reinforces practical learning across chapters - Clear learning objectives and chapter summaries to track progress - Step-by-step exercises and coding activities to build analytical fluency - Examples grounded in real business applications for immediate relevance Whether preparing for exams or building analytical capability for a future career, this textbook equips students with the tools to turn business data into strategic advantage.
R 7,557.83
R 611.00
Advanced Data Analytics in Health
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
R 7,688.40
R 548.28
Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis
This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field.Key Features:• Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journey.• Striking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchers.• The content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalities.• Covering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications.
R 5,671.58
R 580.00
Advanced Data Analytics in Health
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
R 7,423.97
R 1,107.00
Principles of Healthcare Data Analytics
This textbook guides readers in transforming medical information into actionable insights that enhance patient care and operational efficiency. Readers learn to extract meaningful intelligence from diverse sources such as electronic health records, medical imaging, IoT devices, genomic datasets, and patient-generated health information from wearables and mobile applications.
R 4,267.74
Business Analytics with Python
Your essential textbook for mastering business analytics through Python.Business Analytics with Python by Bowei Chen and Gerhard Kling is the definitive guide for upper-level undergraduate and postgraduate students studying business, management or finance. Designed to support analytics modules that prioritize practical application, this textbook introduces students to data-driven decision-making through Python, without assuming a background in computer science. It aligns with course outcomes by integrating statistical, mathematical and machine learning techniques into a unified business context. This textbook takes a holistic approach to business analytics, exploring how Python can be used to interpret and solve real-world problems. From foundational coding skills to the implementation of supervised and unsupervised machine learning methods, students learn how to translate data into insight across key business functions. Through industry-relevant case studies, including customer churn analysis, fraud detection and sales forecasting, learners build confidence in applying analytics to real organizational challenges. Pedagogical features include: - A running case study that reinforces practical learning across chapters - Clear learning objectives and chapter summaries to track progress - Step-by-step exercises and coding activities to build analytical fluency - Examples grounded in real business applications for immediate relevance Whether preparing for exams or building analytical capability for a future career, this textbook equips students with the tools to turn business data into strategic advantage.
R 2,353.68
R 2,997.00
R 531.00
R 1,313.00
Big Data and Health Analytics
Data availability is surpassing existing paradigms for governing, managing, analyzing, and interpreting health data. Big Data and Health Analytics provides frameworks, use cases, and examples that illustrate the role of big data and analytics in modern health care, including how public health information can inform health delivery.Written for health care professionals and executives, this is not a technical book on the use of statistics and machine-learning algorithms for extracting knowledge out of data, nor a book on the intricacies of database design. Instead, this book presents the current thinking of academic and industry researchers and leaders from around the world.Using non-technical language, this book is accessible to health care professionals who might not have an IT and analytics background. It includes case studies that illustrate the business processes underlying the use of big data and health analytics to improve health care delivery.Highlighting lessons learned from the case studies, the book supplies readers with the foundation required for further specialized study in health analytics and data management. Coverage includes community health information, information visualization which offers interactive environments and analytic processes that support exploration of EHR data, the governance structure required to enable data analytics and use, federal regulations and the constraints they place on analytics, and information security.Links to websites, videos, articles, and other online content that expand and support the primary learning objectives for each major section of the book are also included to help you develop the skills you will need to achieve quality improvements in health care delivery through the effective use of data and analytics.
R 6,685.76
Python Data Analysis
Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques. Build scalable workflows for time series, NLP, image analytics, and big data processing. Key FeaturesPrepare, clean, and transform data with Python, pandas, and exploratory data analysis techniquesApply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methodsScale analytics workflows using Dask, Ray, Modin, and PySparkBook DescriptionModern data analysis goes beyond cleaning and visualizing data. Today's practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python's modern data ecosystem.Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You'll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows.Starting with statistical foundations, you'll learn how to clean, transform, wrangle, and visualize data. You'll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches.The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you'll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark.By the end of the book, you'll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.What you will learnPrepare, clean, and transform data for exploratory data analysis and data wranglingAnalyze and visualize data using Python and pandasPerform time series analysis, forecasting, and signal processingApply machine learning with Python using scikit-learn techniquesUse regression, classification, clustering, PCA, and Bayesian methodsPerform sentiment analysis, NLP, graph analytics, and image analyticsAccelerate workflows using Dask, Modin, and RayBuild scalable big data analytics pipelines with PySparkWho this book is forThis book is for data analysts, data scientists, business analysts, statisticians, students, and academic professionals who want to strengthen their Python Data Analysis skills. It is ideal for readers looking to apply data science with Python to real-world problems involving data preparation, visualization, machine learning, NLP, image analytics, and big data processing. A basic understanding of mathematics and working knowledge of Python will help you get the most from this book.
R 1,465.00
Health Services Research and Analytics Using Excel®
Your all-in-one resource for quantitative, qualitative, and spatial analyses in Excel® using current real-world healthcare datasets.Health Services Research and Analytics Using Excel® is a practical resource for graduate and advanced undergraduate students in programs studying healthcare administration, public health, and social work as well as public health workers and healthcare managers entering or working in the field. This book provides one integrated, application-oriented resource for common quantitative, qualitative, and spatial analyses using only Excel. With an easy-to-follow presentation of qualitative and quantitative data, students can foster a balanced decision-making approach to financial data, patient statistical data and utilization information, population health data, quality metrics and cultivate analytical skills that are necessary in a data-driven healthcare world. Whereas Excel is typically thought of as limited to quantitative application, this book expands into other Excel applications based on spatial analysis and data visualization represented through 3D Maps as well as text analysis using the free add-in in Excel.Chapters cover the important methods and statistical analysis tools that a practitioner will face when navigating and analyzing data in the public domain or from internal data collection at their health services organization. Topics covered include importing and working with data in Excel, identifying, categorizing and presenting data, setting bounds and hypothesis testing, testing the mean, checking for patterns, data visualization and spatial analysis, interpreting variance, text analysis and much more. A concise overview of research design also provides helpful background on how to gather and measure useful data prior to analyzing in Excel. Because Excel is the most common data analysis software used in the workplace setting, all case examples, exercises, and tutorials are provided with the latest updates to the Excel® software from Office365 ProPlus and newer versions, including all important "Add-ins" such as 3D Maps, Meaning Cloud, and Power Pivots among others. With numerous practice problems and over 100 step-by-step videos, Health Services Research and Analytics Using Excel® is an extremely practical tool for students and health service professionals who must know how to work with data, how to analyze it, and how to use it to improve outcomes unique to healthcare settings.Key Features:Provides a competency-based analytical approach to health services research using ExcelIncludes applications of spatial analysis and data visualization tools based on 3D Maps in ExcelLists select sources of useful national healthcare data with descriptions and website informationChapters contain case examples and practice problems unique to health servicesAll figures and videos are applicable to Office365 ProPlus Excel® and newer versionsContaining over 100 step-by-step videos of Excel applications covered in the chapters
R 3,494.16
Python: Data Analytics and Visualization
Understand, evaluate, and visualize dataAbout This Book* Learn basic steps of data analysis and how to use Python and its packages* A step-by-step guide to predictive modeling including tips, tricks, and best practices* Effectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner.What You Will Learn* Get acquainted with NumPy and use arrays and array-oriented computing in data analysis* Process and analyze data using the time-series capabilities of Pandas* Understand the statistical and mathematical concepts behind predictive analytics algorithms* Data visualization with Matplotlib* Interactive plotting with NumPy, Scipy, and MKL functions* Build financial models using Monte-Carlo simulations* Create directed graphs and multi-graphs* Advanced visualization with D3In DetailYou will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn.After this, you will move on to a data analytics specialization-predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling.After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examplesThis Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Getting Started with Python Data Analysis, Phuong Vo.T
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Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
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Biostatistics with Python
Learn how to utilize biostatistics with Python for excelling in research and biomedical professions with practical exemplar projectsKey FeaturesBridge the gap between biostatistics and life sciences with PythonWork with practical exercises for real-world data analysis in biology and medicineAccess a portfolio of exemplar projects in the domains of biomedicine, biotechnology, and biologyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThis book leverages the author’s decade-long experience in biostatistics and data science to simplify the practical use of biostatistics with Python. The chapters show you how to clean and describe your data effectively, setting a solid foundation for accurate analysis and proficiency in biostatistical inference to help you draw meaningful conclusions from your data through hypothesis testing and effect size analysis.The book walks you through predictive modeling to harness the power of Python to create robust predictive analytics that can drive your research and professional projects forward. You'll explore clinical biostatistics, learn how to design studies, conduct survival analysis, and synthesize evidence from multiple studies with meta-analysis – skills that are crucial for making informed decisions based on comprehensive data reviews. The concluding chapters will enhance your ability to analyze biological variables, enabling you to perform detailed and accurate data analysis for biological research. This book's unique blend of biostatistics and Python helps you find practical solutions that make complex concepts easy to grasp and apply.By the end of this biostatistics book, you’ll have moved from theoretical knowledge to practical experience, allowing you to perform biostatistical analysis confidently and accurately.What you will learnGet to grips with the basics of biostatistics and Python programmingClean and describe data using PythonFamiliarize yourself with hypothesis testing and effect size analysisExplore predictive modeling in biostatisticsUnderstand clinical study design and survival analysisGain a clear understanding of the meta-analysis of clinical research dataAnalyze biological variables with PythonDiscover practical data analysis for biological researchWho this book is forThis book is for life science professionals, researchers, biomedical professionals, and aspiring biostatisticians who want to integrate biostatistics into their work or research. A basic understanding of life sciences, biology, or medicine is recommended to fully benefit from this book.
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R 1,545.00
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Practical Business Analytics Using R and Python
This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.
R 1,368.00
IoT, Machine Learning and Data Analytics for Smart Healthcare
Machine learning, Internet of Things (IoT) and data analytics are new and fresh technologies that are being increasingly adopted in the field of medicine. This book positions itself at the forefront of this movement, exploring the beneficial applications of these new technologies and how they are gradually creating a smart healthcare system.This book details the various ways in which machine learning, data analytics and IoT solutions are instrumental in disease prediction in smart healthcare. For example, wearable sensors further help doctors and healthcare managers to monitor patients remotely and collect their health parameters in real-time, which can then be used to create datasets to develop machine learning models that can aid in the prediction and detection of any susceptible disease. In this way, smart healthcare can provide novel solutions to traditional medical issues.This book is a useful overview for scientists, researchers, practitioners and academics specialising in the field of intelligent healthcare, as well as containing additional appeal as a reference book for undergraduate and graduate students
R 2,674.00
IoT, Machine Learning and Data Analytics for Smart Healthcare
Machine learning, Internet of Things (IoT) and data analytics are new and fresh technologies that are being increasingly adopted in the field of medicine. This book positions itself at the forefront of this movement, exploring the beneficial applications of these new technologies and how they are gradually creating a smart healthcare system.This book details the various ways in which machine learning, data analytics and IoT solutions are instrumental in disease prediction in smart healthcare. For example, wearable sensors further help doctors and healthcare managers to monitor patients remotely and collect their health parameters in real-time, which can then be used to create datasets to develop machine learning models that can aid in the prediction and detection of any susceptible disease. In this way, smart healthcare can provide novel solutions to traditional medical issues.This book is a useful overview for scientists, researchers, practitioners and academics specialising in the field of intelligent healthcare, as well as containing additional appeal as a reference book for undergraduate and graduate students
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