Statistics With Python
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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.
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.
Modern Statistics
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Author : Ron S. Kenett
language : en
Publisher: Springer Nature
Release Date : 2022-09-20
Modern Statistics written by Ron S. Kenett and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Computers categories.
This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning. Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses. The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/ "In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
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
Comprehensive Guide To Statistics
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Author : Mohit Chatterjee
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Comprehensive Guide To Statistics written by Mohit Chatterjee and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Science categories.
This comprehensive textbook offers an in-depth exploration of various topics in statistics, ranging from probability theory and statistical inference to machine learning and data analysis. It balances theoretical rigor and practical applications, catering to both undergraduate and graduate students, as well as professionals in the field of statistics and related disciplines. The book begins with foundational concepts in probability theory, covering random variables, probability distributions, and expectation. It then delves into statistical inference, discussing estimation, hypothesis testing, and regression analysis. Advanced topics like Bayesian statistics, machine learning algorithms, and resampling methods are also explored. Key strengths of this textbook include clear and concise explanations, numerous examples, and exercises to reinforce learning. The accessible yet rigorous writing style makes complex concepts understandable to readers at various levels of expertise. Modern computational tools and techniques are incorporated, emphasizing practical aspects of statistical analysis in the era of big data. Readers are encouraged to apply their knowledge using software packages like R and Python, enhancing their skills in data analysis and interpretation. This comprehensive and authoritative textbook covers a wide range of topics in statistics, making it an indispensable resource for students, researchers, and practitioners alike. It provides a solid foundation in statistical theory and its real-world applications.
Python For Data Analysis
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Author : Computer Science Academy
language : en
Publisher: Giale Limited
Release Date : 2020-11-15
Python For Data Analysis written by Computer Science Academy and has been published by Giale Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-15 with Computers categories.
Do you want to understand the Data Analysis power? Do you want to learn Python programming language? Well, this book is for you! This guidebook is going to dive into what you need to know to complete a deep data analysis. This book includes: - What the Python language is - How we can benefit from Data Analysis, no matter what industry we are. - How Python is able to work well with the data analysis - How to install and use the NumPy library, one of the best extensions with Python, to help us get our data analysis done. - How to work with the Pandas and IPython extensions so that we are able to get things done with your analysis. - The practical uses of the data analysis to help you get it done. - A look at the Matplotlib library to help you create some of your own visuals with your data. - How to work with data visuals and how they are so important to your work. - And so much more. Even if you have never studied Python language before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now!
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
Practical Data Science With Python
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Author : Nathan George
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-30
Practical Data Science With Python written by Nathan George 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 2021-09-30 with Computers categories.
Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
Statistics For Beginners In Data Science
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Author : Ai Publishing
language : en
Publisher:
Release Date : 2020-04-18
Statistics For Beginners In Data Science written by Ai Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-18 with categories.
Statistics for Beginners in Data Science Statistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists. If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist. As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used. Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python's focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short--Python is simply a high-priority data science tool. How Is This Book Different? The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better. The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python. The main benefit of purchasing this book is you get quick access to all the extra content provided with this book--Python codes, exercises, references, and PDFs--on the publisher's website, at no extra price. You get to experiment with the practical aspects of Data Science right from page 1. Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren't new to Python and data science, you'll find the hands-on projects in this book immensely helpful. The topics covered include: Introduction to Statistics Getting Familiar with Python Data Exploration and Data Analysis Pandas, Matplotlib, and Seaborn for Statistical Visualization Exploring Two or More Variables and Categorical Data Statistical Tests and ANOVA Confidence Interval Regression Analysis Classification Analysis Click the BUY button and download the book now to start learning and coding Python for Data Science.
Foundations Of Statistics For Data Scientists
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Author : Alan Agresti
language : en
Publisher: CRC Press
Release Date : 2021-11-29
Foundations Of Statistics For Data Scientists written by Alan Agresti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Business & Economics categories.
Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds.rwth-aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.