Probability And Statistics For Data Science
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Probability And Statistics For Data Science
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Author : Norman Matloff
language : en
Publisher: CRC Press
Release Date : 2019-06-21
Probability And Statistics For Data Science written by Norman Matloff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-21 with Business & Economics categories.
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.
Probability And Statistics For Data Science
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Author : Ankit Rathi
language : en
Publisher:
Release Date : 2019-02-21
Probability And Statistics For Data Science written by Ankit Rathi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with categories.
As the title says, this book covers all the topics for probability & statistics in context of data science. While working on data science projects, I tried to look for a reference book which can give reader holistic view of probability & statistics useful for data science, but I could not find everything at one place. So every time, I used to look for the term or topic at various places and then used to relate it in context of data science. At the end, I started writing about these topics in my blog (https://medium.com/@rathi.ankit) as my notes on probability & statistics which were well received by data science community.This book is for people who are working in data science field and 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 approach I have taken here is not to reinvent the wheel, so I try to give an intuitive understanding of each topic and if the user wants to dig further on that topic, he can refer to the companion GitHub notebook of this book, scan the QR code given in the book to get the link.
Probability And Statistics For Data Science
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Author : Carlos Fernandez-Granda
language : en
Publisher: Cambridge University Press
Release Date : 2025-06-30
Probability And Statistics For Data Science written by Carlos Fernandez-Granda and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Mathematics categories.
This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book include random variables, nonparametric and parametric models, correlation, estimation of population parameters, hypothesis testing, principal component analysis, and both linear and nonlinear methods for regression and classification. Examples throughout the book draw from real-world datasets to demonstrate concepts in practice and confront readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.
Statistics For Data Scientists
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Author : Maurits Kaptein
language : en
Publisher:
Release Date : 2022
Statistics For Data Scientists written by Maurits Kaptein and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Statistics For Beginners
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Author : Matt Foster
language : en
Publisher:
Release Date : 2020-10-06
Statistics For Beginners written by Matt Foster and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-06 with Mathematics categories.
Become a Professional at Statistical Analysis and Data Science! Would you like to have the ability to implement statistical methods to your business, draw conclusions and make correct decisions based on them? Statistics play an important role in any industry and business. That's why there's a huge market for individuals who know how to conduct statistical analysis. People who have mastered this skill are considered the modern-day fortune tellers. Their abilities allow them to predict future business trends, market demands, profitability, general market risks, and sales volume! It's easy to get lost in the amount of available sources if you're only getting started. Conducting statistical analysis can be challenging for someone who was never properly introduced to the subject. This guide will help you, whether you're an absolute beginner to data science or an experienced data scientist who wants to learn new skills. Here, Matt Foster will teach you how to process data, predict outcomes, and upgrade your company's operations in the long term. Here's what you'll be able to do after reading this book: Understand and use descriptive statistics Use predictive analytics techniques to turn data into valuable information Use different learning algorithms to optimize your efficiency Analyze past data to predict future trends Integrate data from different sources to conduct a full analysis Use Python for files manipulation and handling Conduct data mining and social media analysis Master Python and PHP languages And so much more! If you're determined to learn statistical analysis, but intimidated by the huge amount of information and unfamiliar terminology, this guide will show you that you don't have to own a degree in data or computer science in order to develop this skill. All you need is this instruction manual, written in a clear, understandable way. The mechanisms are thoroughly explained and the step-by-step methods will help you conquer the world of descriptive statistics. If you want to learn how to conduct statistical analysis and implement it in your business, then Get Your Copy Now!
Introduction To Probability And Statistics For Data Scientists With R
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Author : Ronald D. Fricker, Jr.
language : en
Publisher: CreateSpace
Release Date : 2014-05-25
Introduction To Probability And Statistics For Data Scientists With R written by Ronald D. Fricker, Jr. and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-25 with Mathematics categories.
This is the first three chapters of a textbook for data scientists who want to improve how they work with, analyze, and extract information from data. The focus of the textbook is how to appropriately apply statistical methods, both simple and sophisticated, to 21st century data and problems. This book contains the first three chapters: Introduction -- Data Science and Statistics, Descriptive Statistics, and Data Visualization -- as well as the book front matter. Subsequent chapters will be published in 3- to 5-chapter sets as they become available.The textbook is intended for current and future data scientists, and for anyone interested in deriving information from data. It requires some mathematical sophistication on the part of the reader, as well as comfort using computers and statistical software.Data science is a new field that has arisen to exploit the proliferation of data in the modern world. Mathematical statistics dates back to the mid-18th century, where the field began as the systematic collection of population and economic data by nations. The modern practice of statistics – which includes the collection, summarization, and analysis of data – dates to the early 20th century. Today statistical methods are widely used by governments, businesses and other organizations, as well as by all scientific disciplines.It has been said that a data scientist must have a better grasp of statistics than the average computer scientist and a better grasp of programming than the average statistician. This book will give data scientists a firm foundation in statistics.
Practical Statistics For Data Scientists
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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-10
Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-10 with Computers categories.
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Statistics Essentials For Beginner In Data Science
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Author : Jay Mishra
language : en
Publisher: AI Sciences LLC
Release Date : 2019-01-27
Statistics Essentials For Beginner In Data Science written by Jay Mishra and has been published by AI Sciences LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-27 with categories.
***** BUY NOW (will soon return to 24.95 $) *****Are you thinking of learning Statistics fundamentals for Data Science? If you are looking for a beginner book to master Statistics Learning fundamentals for Data Science, this book is for you. Who Should Read this Book?Aspiring data scientists who are looking forward to begin their journey in the vast field of data science. People who are seeking to learn and understand data analysis from its very deep-rooted basics have found the right book. Clear basic concepts make the foundation of a good knowledge base, which ultimately helps to gain sharp insights into this topic further. This book will give you the practical exposure along with its theory explained comprehensively. This book is the perfect compilation for beginners as well as intermediate learners who intend to learn statistics and data analysis techniques. Why this book?This book will guide you step by step from the very basics to how you can start your own data science project. The best part about this book is its structure, it's structured in such a way that integrates practicals along with its theory to make the concepts easily understandable. It will help you to understand a basic concept like mean, median, mode, scatter plot and histograms. Thus ensures no prior knowledge is required to start learning from this book. The content of this book is specially designed to encompass all the concepts that come under the domain of data science. This book will guide you through the problems and concepts of statistics. What is statistics?h2>Most of the people think statistics in data science is something different and more profound than what we learnt in our mathematics classes but it's not. It is the same concept of data collection followed by its organization, interpretation and presentation. Statistics is the key to develop a desired model in machine learning. Using statistics you can convert your raw meaningless chunk of data to a well-structured informative data. What's Inside This Book? Probability & Bayes Theorem, Data Exploration and Analysis Structured Data Estimates Mean and Median Estimates Variability Exploring the data distribution Percentiles and Boxplots Frequency table and Histograms Density Estimates Mode Correlation Categorical and Numeric Data Visualizing Multiple Variables Regression Analysis Clustering Analysis Statistical tests and ANOVA Classification Naïve Bayes Discriminant Analysis Linear regression Logistic Regression Statistical Machine Learning K_Nearest Neighbor Trees Models Bagging and Random Forest Boosting algorithms Principal Component Analysis K_means Clustering Hierarchical Clustering Model Based Clustering Sources & References From AI Sciences PublishingOur books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.Frequently Asked QuestionsQ: Does this book include everything I need to become a data analyst expert?A: Unfortunately, no. This book is designed for readers taking their first steps in statistics and data science and further learning will be required beyond this book to master all aspects. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON *****
Statistics For Data Science
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-17
Statistics For Data Science written by James D. Miller 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 2017-11-17 with Computers categories.
Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples
Probability Statistics And Data Analysis
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Author : Oscar Kempthorne
language : en
Publisher:
Release Date : 1971
Probability Statistics And Data Analysis written by Oscar Kempthorne and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Mathematics categories.
General background; the nature of real populations; Calculus of probability; Some commonly ocurring mathematical distributions; Distributions of functions of random variables; Distribution of sample statistic; Stachartic processes; General outline of data interpretation problems; Goodness of fit of a completely specified model; Parametric models and likelihood theory; Statistical tests.