Statistics Using Python
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Statistics Using Python
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Author : Oswald Campesato
language : en
Publisher: Stylus Publishing, LLC
Release Date : 2023-12-12
Statistics Using Python written by Oswald Campesato and has been published by Stylus Publishing, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-12 with Computers categories.
This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors. The ability to extract meaningful insights from data requires a deep understanding of statistics. The book ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs. Topics include working with data and exploratory analysis, the basics of probability, descriptive and inferential statistics and their applications, metrics for data analysis, probability distributions, hypothesis testing, and more. Appendices on Python and Pandas have been included. From foundational Python concepts to the intricacies of statistics, this book serves as a comprehensive resource for both beginners and seasoned professionals. FEATURES Provides Python code samples to ensure readers can immediately apply what they learn Covers everything from basic data handling to advanced statistical concepts Features downloadable companion files with code samples and figures Includes two appendices, An Introduction to Python and an Introduction to Pandas as refresher material
An Introduction To Statistics With Python
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Author : Thomas Haslwanter
language : en
Publisher: Springer Nature
Release Date : 2022-11-15
An Introduction To Statistics With Python written by Thomas Haslwanter and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-15 with Computers categories.
Now in its second edition, this textbook provides an introduction to 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. For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs. The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis. With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis.
Statistics And Data Visualisation With Python
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Author : Jesus Rogel-Salazar
language : en
Publisher: CRC Press
Release Date : 2023-01-31
Statistics And Data Visualisation With Python written by Jesus Rogel-Salazar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-31 with Social Science categories.
This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
Statistics With Python
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Author : Hyun-Seok Son
language : en
Publisher: Hyun-Seok Son
Release Date : 2024-12-15
Statistics With Python written by Hyun-Seok Son and has been published by Hyun-Seok Son this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-15 with Mathematics categories.
This book introduces a method of approaching statistical analysis using the programming language Python. In this process, the analysis data uses daily stock price data. People generally have aversion to things that are said to be uncertain. Statistics is an academic discipline that provides a starting point for reasonable preparation for aversion or anxiety by specifically indicating the degree of uncertainty according to criteria, and all parts of the environment in which people live become the subject of this field. In other words, statistics can be said to be a method of identifying trends and extracting various information by converting the actions people take under a certain topic into letters or numbers. In essence, people intuitively perform statistical thinking in their daily lives. However, systematic training is needed to make such performance more objective. Daily stock price data is the numerical representation of people's thoughts and actions in the financial market. This is useful data for training statistical analysis. In this text, we will introduce various statistical approaches using financial data. Statistical analysis requires various basic knowledge such as probability and average, and the concepts and calculations of these are not easy. The programming language Python is a great tool for learning these processes systematically. It's like using Excel to perform statistical analysis. However, Python is a more flexible tool because it allows more room for user intervention than Excel. Of course, in order to take advantage of this flexibility, you need to get used to the language called Python. This part is not easy, but once you get used to it, you can perform statistical analysis from a wide variety of perspectives that analysts can think of. Python is a high-level language that is easier to approach than other languages. If you have basic knowledge of this language, you will be able to operate the code in the text without difficulty, and through that process, you will be able to learn the language more systematically. If you are a beginner, you can invest a short amount of time to acquire basic knowledge through various books or learning sites (refer to the author's blog). Chapter 0 of this book introduces the basic parts of Python used to execute various statistical calculations, analysis, probability, and distributions introduced in this book. You can derive quantitative figures, or statistics, to explain the structure of data distributions. In the process of calculating these statistics, descriptive statistics, such as the mean and variance, which can be calculated from the data itself, are introduced in Chapter 1. In addition, these statistics can calculate (inferential) statistics for judging the possibility of what can happen in general situations, and these calculations are based on probability. Chapters 2 and 3 introduce inferential statistics and probability and probability distributions for judging analysis results. Various analysis methods for inferring results based on these are applied and introduced in Chapters 4 to 8. If you are a reader who does not know or is not familiar with Python, I recommend that you focus on understanding the meaning of the results by executing the codes introduced in the text without understanding them. Please do not forget that the Python codes were used to calculate various formulas introduced in the text. Later, when you gain knowledge about Python, you will be able to become familiar with the Python language by understanding the code. I hope that through this book, you will become familiar with unfamiliar statistical thinking and approaches and the use of the Python language.
Python For Probability Statistics And Machine Learning
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Author : José Unpingco
language : en
Publisher: Springer
Release Date : 2016-03-16
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 2016-03-16 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.
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.
Applied Statistics With Python
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Author : Leon Kaganovskiy
language : en
Publisher: CRC Press
Release Date : 2025-03-03
Applied Statistics With Python written by Leon Kaganovskiy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Mathematics categories.
Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics courses at Touro University and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding. This book does not require previous experience with statistics and Python, explaining the basic concepts before developing them into more advanced methods from scratch. Applied Statistics with Python is intended for undergraduate students in business, economics, biology, social sciences, and natural science, while also being useful as a supplementary text for more advanced students. Key Features: Concentrates on more introductory topics such as descriptive statistics, probability, probability distributions, proportion and means hypothesis testing, as well as one-variable regression The book’s computational (Python) approach allows us to study Statistics much more effectively. It removes the tedium of hand/calculator computations and enables one to study more advanced topics Standardized sklearn Python package gives efficient access to machine learning topics Randomized homework as well as exams are provided in the author’s course shell on My Open Math web portal (free)
Applied Univariate Bivariate And Multivariate Statistics Using Python
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Author : Daniel J. Denis
language : en
Publisher: John Wiley & Sons
Release Date : 2021-05-11
Applied Univariate Bivariate And Multivariate Statistics Using Python written by Daniel J. Denis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Mathematics categories.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Learn Data Analysis With Python
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Author : A.J. Henley
language : en
Publisher: Apress
Release Date : 2018-02-22
Learn Data Analysis With Python written by A.J. Henley and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-22 with Computers categories.
Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects. If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. What You Will Learn Get data into and out of Python code Prepare the data and its format Find the meaning of the data Visualize the data using iPython Who This Book Is For Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
Hands On Data Science For Biologists Using Python
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Author : Yasha Hasija
language : en
Publisher: CRC Press
Release Date : 2021-04-08
Hands On Data Science For Biologists Using Python written by Yasha Hasija and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-08 with Computers categories.
Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.