Applied 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.
Applied Statistics With Python
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Author : Leon Kaganovskiy
language : en
Publisher:
Release Date : 2025
Applied Statistics With Python written by Leon Kaganovskiy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Python (Computer program language) categories.
"Applied Statistics with Python concentrates on applied and computational aspects of statistics, focussing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics at Touro College 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, whilst also being useful as a supplementary text for more advanced students"--
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 Statistics And Econometrics
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Author : Bjørnar Karlsen Kivedal
language : en
Publisher: Springer Nature
Release Date : 2024-04-21
Applied Statistics And Econometrics written by Bjørnar Karlsen Kivedal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-21 with Business & Economics categories.
This accessible textbook introduces the foundations of applied econometrics and statistics for undergraduate students. It covers key topics in econometrics by using step-by-step examples in Gretl and R, providing a guide to using statistical software and the tools for econometric analysis in one self-contained resource. Taking a concise, non-technical approach, the book covers topics including simple regression and hypothesis testing, multiple regression with control variables and isolating effects, instrumental variables, dummy variables, non-linear effects, probability models, heteroskedasticity, time series analysis, and other applied statistical tools such as t-tests and chi squared tests. The book uses small data sets to easily facilitate students’ transition from manual statistical calculations to using and understanding statistical software, including step-by-step examples of regression analysis, as well as additional chapters to aid with econometric notation and mathematical prerequisites, and accompanying online exercises and data sets. This book will be a valuable resource for upper undergraduate students taking courses in introductory econometrics and statistics, as well as students in business administration and other fields of study in social sciences utilising quantitative methods. Graduate students may also benefit from the book.
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.
Foundations Of Programming Statistics And Machine Learning For Business Analytics
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Author : Ram Gopal
language : en
Publisher: SAGE Publications Limited
Release Date : 2023-04-22
Foundations Of Programming Statistics And Machine Learning For Business Analytics written by Ram Gopal and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-22 with Business & Economics categories.
Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage. This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice. Key features: · Introduces programming fundamentals using R and Python · Covers data structures, data management and manipulation and data visualization · Includes interactive coding notebooks so that you can build up your programming skills progressively Suitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science. Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick. Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies. Tillman Weyde is Senior Lecturer at City, University of London.
Practitioner S Guide To Data Science
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Author : Hui Lin
language : en
Publisher: CRC Press
Release Date : 2023-05-24
Practitioner S Guide To Data Science written by Hui Lin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-24 with Business & Economics categories.
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!
Introduction To Engineering And Scientific Computing With Python
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Author : David E. Clough
language : en
Publisher: CRC Press
Release Date : 2022-09-07
Introduction To Engineering And Scientific Computing With Python written by David E. Clough and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-07 with Computers categories.
As more and more engineering departments and companies choose to use Python, this book provides an essential introduction to this open-source, free-to-use language. Expressly designed to support first-year engineering students, this book covers engineering and scientific calculations, Python basics, and structured programming. Based on extensive teaching experience, the text uses practical problem solving as a vehicle to teach Python as a programming language. By learning computing fundamentals in an engaging and hands-on manner, it enables the reader to apply engineering and scientific methods with Python, focusing this general language to the needs of engineers and the problems they are required to solve on a daily basis. Rather than inundating students with complex terminology, this book is designed with a leveling approach in mind, enabling students at all levels to gain experience and understanding of Python. It covers such topics as structured programming, graphics, matrix operations, algebraic equations, differential equations, and applied statistics. A comprehensive chapter on working with data brings this book to a close. This book is an essential guide to Python, which will be relevant to all engineers, particularly undergraduate students in their first year. It will also be of interest to professionals and graduate students looking to hone their programming skills, and apply Python to engineering and scientific contexts.
R Programming
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Author : Kingsley Okoye
language : en
Publisher: Springer Nature
Release Date : 2024-07-07
R Programming written by Kingsley Okoye and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-07 with Computers categories.
This book is written for statisticians, data analysts, programmers, researchers, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using R object-oriented programming language and RStudio integrated development environment (IDE). R is an open-source software with a development environment (RStudio) for computing statistics and graphical displays through data manipulation, modeling, and calculation. R packages and supported libraries provide a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical software, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system. Therefore, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the users. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and nonparametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for the reliability and validity of the available datasets. Different research experiments, case scenarios, and examples are explained in this book. The book provides a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations thus congruence of Statistics and Computer programming in Research.
Data Science With Applied Statistics In Python
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Author : Dr.A Manimaran
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-02-05
Data Science With Applied Statistics In Python written by Dr.A Manimaran and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-05 with Language Arts & Disciplines categories.
Dr.A Manimaran, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Selvakumar, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.S. Ramesh, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.J.Chenni Kumaran, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.Sivaram, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.