Download or read book Statistics Notebook written by Kani Notebooks & Journals and published by . This book was released on 2019-09-05 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics Composition Notebook: Wide Ruled Paper Notebook, Features: 120 wide ruled white papers, 60 sheets 8.5" x 11" (22 x 28 cm) dimensions, US letter size large notebook Nice mate (soft) cover Can be used as a composition book, journal, diary, school and work notebooks Suitable for writing, note taking, drawing, handwriting, making lists, journaling and brainstorming ideas Perfect gift for friends, family members, writers, teachers, students, adults and kids who love writing and drawing

Download or read book Statistics with Julia written by Yoni Nazarathy and published by Springer Nature. This book was released on 2021-09-04 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Download or read book An Introduction to Statistics with the Wolfram Language written by Juan H. Klopper and published by Wolfram Media Incorporated. This book was released on 2020-05-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Probability and Statistics for Data Science written by Norman Matloff and published by CRC Press. This book was released on 2019-06-21 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

Download or read book Online Statistics Education written by David M Lane and published by . This book was released on 2014-12-02 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.This print edition of the public domain textbook gives the student an opportunity to own a physical copy to help enhance their educational experience. This part I features the book Front Matter, Chapters 1-10, and the full Glossary. Chapters Include:: I. Introduction, II. Graphing Distributions, III. Summarizing Distributions, IV. Describing Bivariate Data, V. Probability, VI. Research Design, VII. Normal Distributions, VIII. Advanced Graphs, IX. Sampling Distributions, and X. Estimation. Online Statistics Education: A Multimedia Course of Study (http: //onlinestatbook.com/). Project Leader: David M. Lane, Rice University.

Download or read book 9789355517104 written by Wei-Meng Lee and published by BPB Publications. This book was released on 2024-07-17 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION Statistics is an important skill set to have when working as a quality analyst, a mathematician, a data analyst, a software engineer, or any analytical job. This book, "Implementing Statistics with Python," will teach you the basics of statistics and how to use Python to analyze data. You will learn to find patterns, quantify uncertainty, and make data-driven predictions with confidence. You will start with basic statistics and then use Python libraries like NumPy and Pandas for data manipulation. You will also learn data visualization with Matplotlib and Seaborn to create informative charts. The book covers probability theory and statistical inference to help you make data-driven decisions. You will be exploring regression and time series analysis with ARIMA for forecasting. Finally, the book introduces ML algorithms, preparing you for real-world data science projects. The book focuses on applying statistics rather than theory, using popular libraries like NumPy, SciPy, Pandas, Matplotlib, and Scikit-Learn. Reading this book will give you a good foundation for working with ML, business analytics, and data-driven business challenges. KEY FEATURES ● Learn the various aspects of statistics and its applications in real-world scenarios. ● Learn about the various libraries in Python for working with data. ● Adopt the learn-by-doing approach to solve real-world statistics problems. ● Learn how statistics is applied to Machine Learning. WHAT YOU WILL LEARN ● Learn the fundamentals of Python and its libraries like Numpy, Pandas, Matplotlib and Seaborn. ● Grasp descriptive statistics and probability concepts. ● Perform statistical inference with Chi-square, ANOVA, and regression analysis. ● Skillfully navigate multivariate and time series analysis. ● Apply statistical techniques in practical ML. WHO THIS BOOK IS FOR This book is for readers with basic Python knowledge who want to apply statistics in real-life scenarios, and those pursuing careers in data analytics, data engineering, data science, ML, and AI. It is also ideal for students beginning a course in statistics. TABLE OF CONTENTS 1. Introduction to Statistics 2. Python Basics for Statistics 3. Introduction to NumPy and Pandas for Data Manipulation 4. Data Visualization with Matplotlib and Seaborn 5. Descriptive Statistics 6. Probability Theory 7. Statistical Inference 8. Regression Analysis 9. Multivariate Analysis 10. Time Series Analysis 11. Machine Learning for Statistics 12. Practical Statistical Analysis in Machine Learning

Download or read book The Art of Statistics written by David Spiegelhalter and published by Basic Books. This book was released on 2019-09-03 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

Download or read book Normal Distribution written by Joy Journals and published by Createspace Independent Publishing Platform. This book was released on 2018-07-30 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This funny maths & statistics note book with graph paper makes a cool notebook, planner, writing journal or note book for a mathematician or statistician. This notebook is 6" x 8" and has 132 pages of graph paper, perfect to work out the most challenging of maths problems, to draw graphs or to do your calculations. Makes a cool gift for anyone who loves math or statistics and will understand this ghoulish statistics joke!

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Download or read book Data Science with NET and Polyglot Notebooks written by Matt Eland and published by Packt Publishing Ltd. This book was released on 2024-08-30 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: ProgExpand your skillset by learning how to perform data science, machine learning, and generative AI experiments in .NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShell Key Features Learn Conduct a full range of data science experiments with clear explanations from start to finish Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems Access all of the code online as a notebook and interactive GitHub Codespace Purchase of the print or Kindle book includes a free PDF eBook Book Description As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem. What you will learn Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics Train machine learning models with ML.NET for classification and regression tasks Customize ML.NET model training pipelines with AutoML, transforms, and model trainers Apply best practices for deploying models and monitoring their performance Connect to generative AI models using Polyglot Notebooks Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel Create interactive online documentation with Mermaid charts and GitHub Codespaces Who this book is for This book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It’s ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.rammer’s guide to data science using ML.NET, OpenAI, and Semantic Kernel

Download or read book The Science Writers Handbook written by Writers of SciLance and published by Da Capo Lifelong Books. This book was released on 2013-04-30 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Popular science writing has exploded in the past decade, both in print and online. Who better to guide writers striving to succeed in the profession than a group of award-winning independent journalists with a combined total of 225 years of experience? From Thomas Hayden's chapter on the perfect pitch to Emma Maris's advice on book proposals to Mark Schrope's essential information on contracts, the members of SciLance give writers of all experience levels the practical information they need to succeed, as either a staffer or a freelancer. Going beyond craft, The Science Writer's Handbook also tackles issues such as creating productive office space, balancing work and family, and finding lasting career satisfaction. It is the ultimate guide for anyone looking to prosper as a science writer in the new era of publishing.

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Download or read book IPython Interactive Computing and Visualization Cookbook written by Cyrille Rossant and published by Packt Publishing Ltd. This book was released on 2014-09-25 with total page 899 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

Download or read book An Introduction to Statistics with Python written by Thomas Haslwanter and published by Springer. This book was released on 2016-07-20 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Statistics with Mathematica written by Martha L. Abell and published by Academic Press. This book was released on 1999 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers the use of Mathematica for applications ranging from descriptive statistics, through multiple regression and nonparametric methods; uses virtually all of Mathematica's built-in statistical commands, as well as those contained in various Mathematica packages; Additionally, the authors have written numerous procedures to extend Mathematica's capabilities, which are also included on the CD-ROM

Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.