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Author: Kani Notebooks & Journals
Publisher:
ISBN: 9781691176595
Category :
Languages : en
Pages : 122
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Book Description
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
Author: Kani Notebooks & Journals
Publisher:
ISBN: 9781691176595
Category :
Languages : en
Pages : 122
Get Book Here
Book Description
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
Author: Juan H. Klopper
Publisher: Wolfram Media Incorporated
ISBN: 9781579550332
Category :
Languages : en
Pages :
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Book Description
Author: Norman Matloff
Publisher: CRC Press
ISBN: 0429687125
Category : Business & Economics
Languages : en
Pages : 412
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Book Description
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.
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category : Computers
Languages : en
Pages : 609
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Book Description
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
Author: David M Lane
Publisher:
ISBN: 9781687894250
Category :
Languages : en
Pages : 406
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Book Description
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.
Author: Wei-Meng Lee
Publisher: BPB Publications
ISBN: 9355517106
Category : Computers
Languages : en
Pages : 494
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Book Description
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
Author: Matt Eland
Publisher: Packt Publishing Ltd
ISBN: 1835882978
Category : Computers
Languages : en
Pages : 404
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Book Description
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
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Category : Computers
Languages : en
Pages : 553
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Book Description
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
Author: David Spiegelhalter
Publisher: Basic Books
ISBN: 1541618521
Category : Mathematics
Languages : en
Pages : 359
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Book Description
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.
Author: Writers of SciLance
Publisher: Da Capo Lifelong Books
ISBN: 0738216577
Category : Science
Languages : en
Pages : 324
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Book Description
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.
