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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: Kani Notebooks & Journals
Publisher:
ISBN: 9781691177608
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 7.5" x 9.7" (19 x 25 cm) dimensions, medium size 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: 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: Yoni Nazarathy
Publisher: Springer Nature
ISBN: 3030709019
Category : Computers
Languages : en
Pages : 527
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Book Description
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.
Author: Juan H. Klopper
Publisher: Wolfram Media Incorporated
ISBN: 9781579550332
Category :
Languages : en
Pages :
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Book Description
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category : Computers
Languages : en
Pages : 743
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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: 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: Cyrille Rossant
Publisher: Packt Publishing Ltd
ISBN: 178328482X
Category : Computers
Languages : en
Pages : 899
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Book Description
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.
Author: On Target Publishing
Publisher: School Notebooks
ISBN: 9781092606646
Category : Mathematics
Languages : en
Pages : 124
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Book Description
Probability And Statistics Notebook When trying to learn - there are "EASY" ways and "Hard" ways... Having a notebook with plenty of room to both take notes in class, AND do your practice exercises is the easy way! You will soon discover that having a notebook is ESSENTIAL to your success! Here is some of what you are getting: ✧ This 8 x 10 "Probability And Statistics Notebook" paperback book is perfect for you to solve your practice problems and for taking class notes! ✧ By keeping a notebook, you will quickly notice an increase in your focus as well as your problem solving abilities! ✧ Alternating pages - 5x5 graph paper and lined paper - to allow plenty of room for notes and problem solving! This page design makes learning Math a "snap"! ✧ PLUS, there's plenty of space available to make a note of those areas that need a bit more practice - so you don't forget. ✧ The glossy cover is made to industry standards and designed to last. ✧ LARGE 8 x 10 size - plenty of room for showing your work and for notes, yet fits in any backpack or other school book-bag. Take it wherever you go - so it will be handy whenever the urge to study strikes. ✧ Not only is this Notebook large enough for all your needs, it is a full 123 pages in length. ✧ This blank composition Notebook makes a great gift for any math student. Scroll up and grab YOUR copy of "Probability And Statistics Notebook" RIGHT NOW!
Author: Marc Wintjen
Publisher: Packt Publishing Ltd
ISBN: 1838825096
Category : Computers
Languages : en
Pages : 309
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Book Description
Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
