Search results for "survival analysis with python"
Items we need to order for you
Data Science with Python
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook DescriptionData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is forData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
R 1,335.00
R 580.00
Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
R 10,210.61
Introduction to R and Python for Data Analysis
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/randpythonbook/tree/master/data. Instructors can access the solutions manual via the book's website.Key features: - Teaches R and Python in a "side-by-side" way. - Examples are tailored to aspiring data scientists and statisticians, not software engineers. - Designed for introductory graduate students.- Does not assume any mathematical background.
R 5,165.00
Python for Accounting and Finance
Python for Accounting and Finance: A Mind-Mapping Approach is an innovative textbook written for accounting and finance students and professionals with no prior coding experience. It introduces Python programming through a mind mapping approach that presents complex concepts in a clear and structured way to support understanding and retention.The book is organised into four parts: Python Basics, Data Analysis and Visualisation, Automation, and Machine Learning. It places programming within practical accounting and finance contexts so that learners can see the direct application of coding in their field. The textbook uses mind maps as a core instructional method. These visual diagrams show programming concepts and their connections, helping learners to understand, organise and recall information, particularly those who learn through visual representation.Enhanced with Google Colab notebooks, this book creates a highly supportive learning experience, equipping students of accounting and finance with essential programming skills for their speciality.
R 8,572.00
R 1,373.00
R 592.00
What Every Engineer Should Know About Python
Engineers across all disciplines can benefit from learning Python. This powerful programming language enables engineers to enhance their skill sets and perform more sophisticated work in less time, whether in engineering analysis, system design and development, integration and testing, machine learning and other artificial intelligence applications, project management, or other areas. What Every Engineer Should Know About Python offers students and practicing engineers a straightforward and practical introduction to Python for technical programming and broader uses to enhance productivity. It focuses on the core features of Python most relevant to engineering tasks, avoids computer science jargon, and emphasizes writing useful software while effectively leveraging generative AI.Features examples tied to real-world engineering scenarios that are easily adaptedExplains how to leverage the vast ecosystem of open-source Python packages for scientific applications, rather than developing new software from scratchCovers the incorporation of Python into engineering designs and systems, whether web-based, desktop, or embeddedProvides guidance on optimizing generative AI with Python, including case study examplesDescribes software tool environments and development practices for the rapid creation of high-quality softwareDemonstrates how Python can improve personal and organizational productivity through workflow automationDirects readers to further resources for exploring advanced Python featuresThis practical and concise book serves as a self-contained introduction for engineers and readers from scientific disciplines who are new to programming or to Python.
R 8,262.27
R 885.00
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.
R 7,787.80
R 824.00
R 519.00
R 611.00
R 489.00
Hands-On Simulation Modeling with Python
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with easeKey FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook DescriptionSimulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is forThis book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.
R 1,693.00
R 1,047.00
Hands-On Data Structures and Algorithms with Python
Learn to implement complex data structures and algorithms using PythonKey FeaturesUnderstand the analysis and design of fundamental Python data structuresExplore advanced Python concepts such as Big O notation and dynamic programmingLearn functional and reactive implementations of traditional data structuresBook DescriptionData structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications.This book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail.By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms.What you will learnUnderstand object representation, attribute binding, and data encapsulationGain a solid understanding of Python data structures using algorithmsStudy algorithms using examples with pictorial representationLearn complex algorithms through easy explanation, implementing PythonBuild sophisticated and efficient data applications in PythonUnderstand common programming algorithms used in Python data scienceWrite efficient and robust code in Python 3.7Who this book is forThis book is for developers who want to learn data structures and algorithms in Python to write complex and flexible programs. Basic Python programming knowledge is expected.
R 1,498.00
Python Essentials
This text describes the essentials of the Python programming language for symbolic and numeric engineering problems, emphasizing an interactive approach. It incorporates the Spyder IDE in conjunction with the four Python packages (NumPy, SciPy, SymPy, and MatplotLib) most frequently used in engineering and the sciences. The book is divided into four parts. The first part, chapter 1, outlines the environment for using Python in an interactive mode and provides descriptions of the many functions and operations available in that environment for numerical and symbolic problem-solving and for basic 2-D plotting of results. For users with no or little experience with Python the chapter can serve as a tutorial while for users with some Python experience the chapter can serve as a reference guide. The second part of the book, contained in chapters 2 and 3, discusses vectors and arrays (matrices) from both a mathematical perspective as well as from a physical perspective. Engineering examples are provided in the chapters and the exercises. Chapters 3, 4, 5 and 8 are the third part of the book, covering the solution of 1) linear simultaneous algebraic equations, 2) nonlinear algebraic equations, 3) ordinary differential equations, and 4) partial differential equations, respectively. These topics again are covered from both a mathematical and physical standpoint with solutions obtained in engineering examples and exercises. Python animations are also introduced as part of the solutions found for ordinary differential equations. Finally, the fourth part of the book, chapter 7, covers the use of 2-D and 3-D plotting as well as symbolic plotting in detail, expanding significantly on the short introduction given in chapter 1.
R 2,406.00
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 journeyStriking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchersThe content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalitiesCovering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications
R 2,674.00
Student's Guide to Data and Error Analysis
All students taking laboratory courses within the physical sciences and engineering will benefit from this book, whilst researchers will find it an invaluable reference. This concise, practical guide brings the reader up-to-speed on the proper handling and presentation of scientific data and its inaccuracies. It covers all the vital topics with practical guidelines, computer programs (in Python), and recipes for handling experimental errors and reporting experimental data. In addition to the essentials, it also provides further background material for advanced readers who want to understand how the methods work. Plenty of examples, exercises and solutions are provided to aid and test understanding, whilst useful data, tables and formulas are compiled in a handy section for easy reference.
R 3,751.13
R 1,099.00
Scientific Data Analysis using Jython Scripting and Java
Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.
R 1,582.06
Handbook of Computer Programming with Python
This handbook provides a hands-on experience based on the underlying topics, and assists students and faculty members in developing their algorithmic thought process and programs for given computational problems. It can also be used by professionals who possess the necessary theoretical and computational thinking background but are presently making their transition to Python.Key Features:Discusses concepts such as basic programming principles, OOP principles, database programming, GUI programming, application development, data analytics and visualization, statistical analysis, virtual reality, data structures and algorithms, machine learning, and deep learning Provides the code and the output for all the concepts discussedIncludes a case study at the end of each chapterThis handbook will benefit students of computer science, information systems, and information technology, or anyone who is involved in computer programming (entry-to-intermediate level), data analytics, HCI-GUI, and related disciplines.
R 5,765.50
Dynamic Regression Models for Survival Data
This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables. Use of the suggested models and methods is illustrated on real data examples, using the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets.
R 4,683.98
R 1,191.00
Soil Physics with Python
This innovative study presents concepts and problems in soil physics, and provides solutions using original computer programs. It provides a close examination of physical environments of soil, including an analysis of the movement of heat, water and gases. The authors employ the programming language Python, which is now widely used for numerical problem solving in the sciences. In contrast to the majority of the literature on soil physics, this text focuses onsolving, not deriving, differential equations for transport. Using numerical procedures to solve differential equations allows the solution of quite difficult problems with fairly simple mathematical tools. Numerical methods convert differential into algebraic equations, which can be solved usingconventional methods of linear algebra. Each chapter introduces a soil physics concept, and proceeds to develop computer programs to solve the equations and illustrate the points made in the discussion.Problems at the end of each chapter help the reader practise using the concepts introduced. The text is suitable for advanced undergraduates, graduates and researchers of soil physics. It employs an open source philosophy where computer code is presented, explained and discussed, and provides the reader with a full understanding of the solutions. Once mastered, the code can be adapted and expanded for the user's own models, fostering further developments. The Python tools provide a simplesyntax, Object Oriented Programming techniques, powerful mathematical and numerical tools, and a user friendly environment.
R 2,669.00
Statistical Analysis of Doubly Truncated Data
A thorough treatment of the statistical methods used to analyze doubly truncated data In The Statistical Analysis of Doubly Truncated Data, an expert team of statisticians delivers an up-to-date review of existing methods used to deal with randomly truncated data, with a focus on the challenging problem of random double truncation. The authors comprehensively introduce doubly truncated data before moving on to discussions of the latest developments in the field. The book offers readers examples with R code along with real data from astronomy, engineering, and the biomedical sciences to illustrate and highlight the methods described within. Linear regression models for doubly truncated responses are provided and the influence of the bandwidth in the performance of kernel-type estimators, as well as guidelines for the selection of the smoothing parameter, are explored. Fully nonparametric and semiparametric estimators are explored and illustrated with real data. R code for reproducing the data examples is also provided. The book also offers: A thorough introduction to the existing methods that deal with randomly truncated dataComprehensive explorations of linear regression models for doubly truncated responsesPractical discussions of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameterIn-depth examinations of nonparametric and semiparametric estimators Perfect for statistical professionals with some background in mathematical statistics, biostatisticians, and mathematicians with an interest in survival analysis and epidemiology, The Statistical Analysis of Doubly Truncated Data is also an invaluable addition to the libraries of biomedical scientists and practitioners, as well as postgraduate students studying survival analysis.
R 3,353.00
Scientific Data Analysis using Jython Scripting and Java
Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.
R 1,582.96
R 1,527.00
Analysis and Presentation of Experimental Results
This book is intended as a guide to the analysis and presentation of experimental results. It develops various techniques for the numerical processing of experimental data, using basic statistical methods and the theory of errors. After presenting basic theoretical concepts, the book describes the methods by which the results can be presented, both numerically and graphically. The book is divided into three parts, of roughly equal length, addressing the theory, the analysis of data, and the presentation of results. Examples are given and problems are solved using the Excel, Origin, Python and R software packages. In addition, programs in all four languages are made available to readers, allowing them to use them in analyzing and presenting the results of their own experiments. Subjects are treated at a level appropriate for undergraduate students in the natural sciences, but this book should also appeal to anyone whose work involves dealing with experimental results.
R 3,114.00
R 793.00
Hands-On AI Trading with Python, QuantConnect, and AWS
Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.Predict market volatility regimes and allocate funds accordingly.Predict daily returns of tech stocks using classifiers.Forecast Forex pairs' future prices using Support Vector Machines and wavelets.Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.
R 1,254.00
R 1,099.00
R 486.00
R 1,099.00
R 458.00
R 730.10
R 794.00
Optical Electromagnetic Field Analysis Using Python
In this book, Kajikawa and Okamoto explain how to use Python to calculate and visualize the optical response of microscopic structures and systems. Throughout, the authors provide varied examples to instruct readers in the application of theoretical knowledge to real-world scenarios.Electromagnetic field analysis is often necessary to determine the optical response of materials with microscopic structures. Although the principles are widely described, the manual calculation and visualization of results are not and remain challenging. Python is the ideal language to use for this as it has a large functional library for visualizing analysis results and is suitable for programming beginners to use at low cost, so it has many advantages over languages like Fortran, BASIC, and C. Here, the authors introduce the application of Python to various electromagnetic field analysis scenarios in the field of nanophotonics. The first half of the book describes cases in which there is an analytical solution for the structure and addresses scenarios such as scattering and absorption in spherical and cylindrical structures and complex structures such as rotating ellipsoids, sphere-aggregated structures, and hemispherical structures. The second half describes methods including rigorous coupling wave analysis, finite-difference time-domain method and discrete dipole approximation for numerically solving varied structures. This book enables readers to conduct their own electromagnetic field analysis quickly, cheaply, and accurately without in-depth study of other complicated and time-consuming approaches or programs.This book is invaluable for researchers and postgraduate students working in the fields of optics and photonics. Additionally, the contents are useful not only for those conducting electromagnetic field analysis but also those simulating physical, chemical, and biological phenomena.
R 7,576.00
