Survival Analysis With Python
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Survival Analysis With Python
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Author : Avishek Nag
language : en
Publisher: CRC Press
Release Date : 2021-12-17
Survival Analysis With Python written by Avishek Nag and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-17 with Computers categories.
Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-06-30
An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Mathematics categories.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Biostatistics With Python
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Author : Darko Medin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-11-29
Biostatistics With Python written by Darko Medin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
Learn how to utilize biostatistics with Python for excelling in research and biomedical professions with practical exemplar projects Key Features Bridge the gap between biostatistics and life sciences with Python Work with practical exercises for real-world data analysis in biology and medicine Access a portfolio of exemplar projects in the domains of biomedicine, biotechnology, and biology Purchase of the print or Kindle book includes a free PDF eBook Book 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 learn Get to grips with the basics of biostatistics and Python programming Clean and describe data using Python Familiarize yourself with hypothesis testing and effect size analysis Explore predictive modeling in biostatistics Understand clinical study design and survival analysis Gain a clear understanding of the meta-analysis of clinical research data Analyze biological variables with Python Discover practical data analysis for biological research Who this book is for This 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.
Python Automation Mastery
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Author : Rob Botwright
language : en
Publisher: Rob Botwright
Release Date : 2023
Python Automation Mastery written by Rob Botwright and has been published by Rob Botwright this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Technology & Engineering categories.
🚀 PYTHON AUTOMATION MASTERY: From Novice to Pro Book Bundle 🚀 Are you ready to unlock the full potential of Python for automation? Look no further than the "Python Automation Mastery" book bundle, a comprehensive collection designed to take you from a beginner to an automation pro! 📘 Book 1 - Python Automation Mastery: A Beginner's Guide · Perfect for newcomers to programming and Python. · Learn Python fundamentals and the art of automation. · Start automating everyday tasks right away! 📗 Book 2 - Python Automation Mastery: Intermediate Techniques · Take your skills to the next level. · Discover web scraping, scripting, error handling, and data manipulation. · Tackle real-world automation challenges with confidence. 📙 Book 3 - Python Automation Mastery: Advanced Strategies · Explore advanced automation concepts. · Master object-oriented programming and external libraries. · Design and implement complex automation projects. 📕 Book 4 - Python Automation Mastery: Expert-Level Solutions · Become an automation architect. · Handle high-level use cases in AI, network security, and data analysis. · Elevate your automation skills to expert status. 🌟 What Makes This Bundle Special? · Comprehensive journey from novice to pro in one bundle. · Easy-to-follow, step-by-step guides in each book. · Real-world examples and hands-on exercises. · Learn ethical automation practices and best strategies. · Access a treasure trove of automation knowledge. 🚀 Why Python? Python is the go-to language for automation due to its simplicity and versatility. Whether you're looking to streamline everyday tasks or tackle complex automation challenges, Python is your ultimate tool. 📈 Invest in Your Future Automation skills are in high demand across industries. By mastering Python automation, you'll enhance your career prospects, supercharge your productivity, and become a sought-after automation expert. 📚 Grab the Complete Bundle Now! Don't miss out on this opportunity to become a Python automation master. Get all four books in one bundle and embark on your journey from novice to pro. Buy now and transform your Python skills into automation mastery!
Survival Analysis
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Author : David G. Kleinbaum
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-18
Survival Analysis written by David G. Kleinbaum and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-18 with Medical categories.
This text on smvival analysis methods contains the following chapters: 1 Introduction to Smvival Analysis 2 Kaplan-Meier Survival Curves and the Log-Rank Test 3 The Cox Proportional Hazards Model and Its Characteristics 4 Evaluating the Proportional Hazards Assumption 5 The Stratified Cox Procedure 6 Extension of the Cox Proportional Hazards Model for Time Dependent Variables Each chapter contains a presentation of its topic in '1ecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that high light the main points, formulae, or examples being presented. The reader may also purchase directly from the author audio cassette tapes of each chapter. The use of the audiotape with the illustrations and formu lae, ignoring the script, is intended to be similar to a lecture. Tapes may be obtained by writing or calling the author at the following address: Depart ment of Epidemiology, Rollins School of Public Health, Emory University, 1518 CliftoIi Rd. N. E. , Atlanta, GA 30322; phone (404) 727-9667. This text is intended for self-study.
Python For Data Analysis
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Author : Dr.Vidya Santosh Dhamdhere
language : en
Publisher: RK Publication
Release Date : 2024-07-25
Python For Data Analysis written by Dr.Vidya Santosh Dhamdhere and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Computers categories.
Python for Data Analysis the essential tools and techniques for data manipulation, cleaning, and analysis in Python. It emphasizes the use of libraries like pandas, NumPy, and Matplotlib to efficiently handle and visualize data. Ideal for analysts and aspiring data scientists, the book provides practical insights, examples, and workflows for handling real-world datasets. Whether for beginners or experienced professionals, it delivers a solid foundation in Python's data analysis ecosystem.
Python For Probability Statistics And Machine Learning
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Author : José Unpingco
language : en
Publisher: Springer
Release Date : 2019-06-29
Python For Probability Statistics And Machine Learning written by José Unpingco and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-29 with Technology & Engineering categories.
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Pandas For Everyone
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Author : Daniel Y. Chen
language : en
Publisher: Addison-Wesley Professional
Release Date : 2018
Pandas For Everyone written by Daniel Y. Chen and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computers categories.
Pandas dataframe basics -- Pandas data structures -- Introduction to plotting -- Data assembly -- Missing data -- Tidy data -- Data types -- Strings and text data -- Apply -- Groupby operations : split-apply-combine -- The datetime data type -- Linear models -- Generalized linear models -- Model diagnostics -- Regularization -- Clustering -- Life outside of pandas -- Toward a self-directed learner.
Sparse Estimation With Math And Python
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Author : Joe Suzuki
language : en
Publisher: Springer Nature
Release Date : 2021-10-30
Sparse Estimation With Math And Python written by Joe Suzuki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-30 with Computers categories.
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis. This book is one of a series of textbooks in machine learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679) Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762) Sparse Estimation with Math and R
An Introduction To Statistics With Python
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Author : Thomas Haslwanter
language : en
Publisher: Springer
Release Date : 2016-07-20
An Introduction To Statistics With Python written by Thomas Haslwanter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-20 with Computers categories.
This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.