Search results for "Health Analytics with Python"
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AI-Assisted Statistics for Data Scientists
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and how they can support real-world statistical workflows.This book highlights concepts that matter most when working with data, building predictive models, and deploying AI responsibly. If you're comfortable with R or Python and have had some exposure to basic statistics, this concise reference will boost your statistical literacy, your understanding of how AI works, and your confidence in real-world data science and AI projects.Conduct exploratory analysis of data to improve quality and model outcomesApply sampling and experimental design to reduce bias and answer questions with clarityUse regression to understand data-generating processes and detect anomaliesBuild predictive models using classification, clustering, and unsupervised learning with unbalanced data
R 1,824.00
R 855.96
Applying Analytics
Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practical Introduction systematically teaches novices how to apply algorithms to real data and how to recognize potential pitfalls. It offers one of the first textbooks for the emerging first course in analytics.The text concentrates on the interpretation, strengths, and weaknesses of analytical techniques, along with challenges encountered by analysts in their daily work. The author shares various lessons learned from applying analytics in the real world. He supplements the technical material with coverage of professional skills traditionally learned through experience, such as project management, analytic communication, and using analysis to inform decisions. Example data sets used in the text are available for download online so that readers can test their own analytic routines. Suitable for beginning analysts in the sciences, business, engineering, and government, this book provides an accessible, example-driven introduction to the emerging field of analytics. It shows how to interpret data and identify trends across a range of fields.
R 3,826.67
Bayesian Modeling and Computation in Python
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory.The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics.This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.
R 5,165.00
Introductory Statistics and Analytics
Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical conceptsNumerous interactive links designed to provide solutions to exercises and further information on crucial conceptsLinkages that connect statistics to the rapidly growing field of data scienceMultiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze dataAreas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.
R 2,441.00
Computational and Statistical Methods for Analysing Big Data with Applications
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.
R 2,941.00
