An Introduction To Statistics With Python
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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.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-08-01
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 2023-08-01 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.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer
Release Date : 2023-07-01
An Introduction To Statistical Learning written by Gareth James and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 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.
Introduction To Data Science
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Author : Laura Igual
language : en
Publisher: Springer
Release Date : 2017-02-22
Introduction To Data Science written by Laura Igual and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-22 with Computers categories.
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
A Hands On Introduction To Data Science
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Author : Chirag Shah
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-02
A Hands On Introduction To Data Science written by Chirag Shah and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Business & Economics categories.
An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.
Statistics For Machine Learning
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Author : Himanshu Singh
language : en
Publisher: BPB Publications
Release Date : 2021-01-15
Statistics For Machine Learning written by Himanshu Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-15 with Computers categories.
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem Ê KEY FEATURESÊ _ Develop a Conceptual and Mathematical understanding of Statistics _ Get an overview of Statistical Applications in Python _ Learn how to perform Hypothesis testing in Statistics _ Understand why Statistics is important in Machine Learning _ Learn how to process data in Python Ê DESCRIPTIONÊÊ This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc.Ê You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning. Ê WHAT YOU WILLÊ LEARNÊÊ _ Understand the basics of Statistics _ Get to know more about Descriptive Statistics _ Understand and learn advanced Statistics techniques _ Learn how to apply Statistical concepts in Python _ Understand important Python packages for Statistics and Machine Learning Ê WHO THIS BOOK IS FORÊ This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite. TABLE OF CONTENTSÊ 1. Introduction to Statistics 2. Descriptive Statistics 3. Probability 4. Random Variables 5. Parameter Estimations 6. Hypothesis Testing 7. Analysis of Variance 8. Regression 9. Non Parametric Statistics 10. Data Analysis using Python 11. Introduction to Machine Learning
Introduction To Python In Earth Science Data Analysis
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Author : Maurizio Petrelli
language : en
Publisher: Springer Nature
Release Date : 2021-09-16
Introduction To Python In Earth Science Data Analysis written by Maurizio Petrelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-16 with Science categories.
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
Introduction To Data Systems
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Author : Thomas Bressoud
language : en
Publisher: Springer Nature
Release Date : 2020-12-04
Introduction To Data Systems written by Thomas Bressoud and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.
Introduction To Data Science For Social And Policy Research
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Author : Jose Manuel Magallanes Reyes
language : en
Publisher: Cambridge University Press
Release Date : 2017-09-21
Introduction To Data Science For Social And Policy Research written by Jose Manuel Magallanes Reyes and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-21 with Mathematics categories.
This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.
The Statistics And Calculus With Python Workshop
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Author : Peter Farrell
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-08-18
The Statistics And Calculus With Python Workshop written by Peter Farrell 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 2020-08-18 with Computers categories.
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy Key FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook Description Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges. What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is for If you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.