Essential Statistics For Data Science A Concise Crash Course

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Essential Statistics for Data Science: A Concise Crash Course

Author : Mu Zhu
Publisher : Oxford University Press
ISBN 13 : 019269359X
Total Pages : 177 pages
Book Rating : 4.1/5 (926 download)

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Book Synopsis Essential Statistics for Data Science: A Concise Crash Course by : Mu Zhu

Download or read book Essential Statistics for Data Science: A Concise Crash Course written by Mu Zhu and published by Oxford University Press. This book was released on 2023-04-04 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part text introduces readers to the basics of probability and random variables and guides them towards relatively advanced topics in both frequentist and Bayesian in a matter of weeks. Part I, Talking Probability explains the statistical approach to analysing data with a probability model to describe the data generating process. Part II, Doing Statistics demonstrates how the unknown quantities in data i.e. it's parameters is applicable in statistical interference. Part III, Facing Uncertainty explains the importance of explicity describing how much uncertainty is caused by parameters with intrinsic scientific meaning and how to take that into account when making decisions. Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more focused than a typical undergraduate text, but still lighter and more accessible than an average graduate text.

Essential Statistics for Data Science

Author : Mu Zhu
Publisher : Oxford University Press
ISBN 13 : 0192867733
Total Pages : 177 pages
Book Rating : 4.1/5 (928 download)

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Book Synopsis Essential Statistics for Data Science by : Mu Zhu

Download or read book Essential Statistics for Data Science written by Mu Zhu and published by Oxford University Press. This book was released on 2023-07-31 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three part text introduces readers to the basics of probability and random variables and guides them towards relatively advanced topics in both frequentist and Bayesian in a matter of weeks. Part I, Talking Probability explains the statistical approach to analysing data with a probability model to describe the data generating process. Part II, Doing Statistics demonstrates how the unknown quantities in data i.e. it's parameters is applicable in statistical interference. Part III, Facing Uncertainty explains the importance of explicity describing how much uncertainty is caused by parameters with intrinsic scientific meaning and how to take that intoaccount when making decisions. Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more focused than a typical undergraduate text, but still lighter and more accessible than an average graduate text.

Statistics Crash Course for Beginners

Author : Ai Publishing
Publisher :
ISBN 13 : 9781734790160
Total Pages : 330 pages
Book Rating : 4.7/5 (91 download)

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Book Synopsis Statistics Crash Course for Beginners by : Ai Publishing

Download or read book Statistics Crash Course for Beginners written by Ai Publishing and published by . This book was released on 2020-11-11 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Frequentist and Bayesian Statistics Crash Course for Beginners Data and statistics are the core subjects of Machine Learning (ML). The reality is the average programmer may be tempted to view statistics with disinterest. But if you want to exploit the incredible power of Machine Learning, you need a thorough understanding of statistics. The reason is a Machine Learning professional develops intelligent and fast algorithms that learn from data. Frequentist and Bayesian Statistics Crash Course for Beginners presents you with an easy way of learning statistics fast. Contrary to popular belief, statistics is no longer the exclusive domain of math Ph.D.s. It's true that statistics deals with numbers and percentages. Hence, the subject can be very dry and boring. This book, however, transforms statistics into a fun subject. Frequentist and Bayesian statistics are two statistical techniques that interpret the concept of probability in different ways. Bayesian statistics was first introduced by Thomas Bayes in the 1770s. Bayesian statistics has been instrumental in the design of high-end algorithms that make accurate predictions. So even after 250 years, the interest in Bayesian statistics has not faded. In fact, it has accelerated tremendously. Frequentist Statistics is just as important as Bayesian Statistics. In the statistical universe, Frequentist Statistics is the most popular inferential technique. In fact, it's the first school of thought you come across when you enter the statistics world. How Is This Book Different? AI Publishing is completely sold on the learning by doing methodology. We have gone to great lengths to ensure you find learning statistics easy. The result: you will not get stuck along your learning journey. This is not a book full of complex mathematical concepts and difficult equations. You will find that the coverage of the theoretical aspects of statistics is proportionate to the practical aspects of the subject. The book makes the reading process easier by presenting you with three types of box-tags in different colors. They are: Requirements, Further Readings, and Hands-on Time. The final chapter presents two mini-projects to give you a better understanding of the concepts you studied in the previous eight chapters. The main feature is you get instant access to a treasure trove of all the related learning material when you buy this book. They include PDFs, Python codes, exercises, and references--on the publisher's website. You get access to all this learning material at no extra cost. You can also download the Machine Learning datasets used in this book at runtime. Alternatively, you can access them through the Resources/Datasets folder. The quick course on Python programming in the first chapter will be immensely helpful, especially if you are new to Python. Since you can access all the Python codes and datasets, a computer with the internet is sufficient to get started. The topics covered include: A Quick Introduction to Python for Statistics Starting with Probability Random Variables and Probability Distributions Descriptive Statistics: Measure of Central Tendency and Spread Exploratory Analysis: Data Visualization Statistical Inference Frequentist Inference Bayesian Inference Hands-on Projects Click the BUY NOW button and start your Statistics Learning journey.

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952938
Total Pages : 317 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 for Data Science

Author : James D. Miller
Publisher : Packt Publishing Ltd
ISBN 13 : 178829534X
Total Pages : 279 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Statistics for Data Science by : James D. Miller

Download or read book Statistics for Data Science written by James D. Miller and published by Packt Publishing Ltd. This book was released on 2017-11-17 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : O'Reilly Media
ISBN 13 : 1492072915
Total Pages : 363 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by O'Reilly Media. This book was released on 2020-04-10 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Practical Statistics for Data Scientists

Author : Peter C. Bruce
Publisher :
ISBN 13 : 9781491952955
Total Pages : 298 pages
Book Rating : 4.9/5 (529 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter C. Bruce

Download or read book Practical Statistics for Data Scientists written by Peter C. Bruce and published by . This book was released on 2017 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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"--Provided by publisher.

All of Statistics

Author : Larry Wasserman
Publisher : Springer Science & Business Media
ISBN 13 : 0387217363
Total Pages : 446 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis All of Statistics by : Larry Wasserman

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.

Introductory Statistics and Analytics

Author : Peter C. Bruce
Publisher : John Wiley & Sons
ISBN 13 : 1118881338
Total Pages : 320 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Introductory Statistics and Analytics by : Peter C. Bruce

Download or read book Introductory Statistics and Analytics written by Peter C. Bruce and published by John Wiley & Sons. This book was released on 2015-01-08 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical concepts Numerous interactive links designed to provide solutions to exercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing field of data science Multiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze data Areas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.

Foundations of Statistics for Data Scientists

Author : Alan Agresti
Publisher : CRC Press
ISBN 13 : 1000462919
Total Pages : 486 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Foundations of Statistics for Data Scientists by : Alan Agresti

Download or read book Foundations of Statistics for Data Scientists written by Alan Agresti and published by CRC Press. This book was released on 2021-11-22 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

Statistics Crash Course

Author : Introbooks
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979344388
Total Pages : 38 pages
Book Rating : 4.3/5 (443 download)

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Book Synopsis Statistics Crash Course by : Introbooks

Download or read book Statistics Crash Course written by Introbooks and published by Createspace Independent Publishing Platform. This book was released on 2017-10-31 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: A crash course in statistics delves into key statistical methods, namely Chi Square, t-test, ANOVA and descriptive statistics. It equally gives an overview of statistical methods as well as various discussions of the statistical tests relating to various database culled from various sources, like the survey of student spending on textbooks, etc. Also, detailed demonstration of various data analysis in SPSS was considered via statistical test.

Essential Statistics

Author : Robert Gould
Publisher :
ISBN 13 : 9780135760284
Total Pages : pages
Book Rating : 4.7/5 (62 download)

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Book Synopsis Essential Statistics by : Robert Gould

Download or read book Essential Statistics written by Robert Gould and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is about understanding how statistical inference and data analysis can improve the world by helping us see more clearly"--

Python Crash Course for Data Analysis: A Complete Beginner Guide for Python Coding, NumPy, Pandas and Data Visualization

Author : Ai Publishing
Publisher : AI Publishing LLC
ISBN 13 : 9781733042642
Total Pages : 168 pages
Book Rating : 4.0/5 (426 download)

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Book Synopsis Python Crash Course for Data Analysis: A Complete Beginner Guide for Python Coding, NumPy, Pandas and Data Visualization by : Ai Publishing

Download or read book Python Crash Course for Data Analysis: A Complete Beginner Guide for Python Coding, NumPy, Pandas and Data Visualization written by Ai Publishing and published by AI Publishing LLC. This book was released on 2019-09-22 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: **GET YOUR COPY NOW, the price will be 21.99$ soon**Learn Python coding for Data Analysis from scratch very easilyWelcome to the Python Crash Course for Data Analysis!The book offers you a solid introduction to the world of Python Coding for data analysis. In this book, you'll learn fundamentals that will enable you to go further in Python Coding, launch or advance a career, and join the next generation of Data Analyst talent that will help define a beneficial, new, powered future for our world. You will study important libraries such as NumPy, Pandas and some Data Visualization libraries.Educational Objectives: This introductory book teaches the foundational skills all Python programmers use to analyze data. It is ideal for beginners who want to learn Python coding or Python for Data Analysis, make informed choices about career goals, and set themselves up for success in this path. At the end of this learning, you will become an great Python Programmer for data Analysis, and learn to analyse data using frameworks like NumPy, Pandas and Matplotlib. Prerequisites: No prior experience with programming is required. You will need to be comfortable with basic computer skills, such as managing files, running programs, and using a web browser to navigate the Internet.You will need to be self-driven and genuinely interested in the Python Coding. No matter how well structured the program is, any attempt to learn programming will involve many hours of studying, practice, and experimentation. Success in this book requires devoting at least 10 hours to your work. This requires some tenacity, and it is especially difficult to do if you don't find Python coding interesting or aren't willing to play around and tinker with your code-so drive, curiosity, and an adventurous attitude are highly recommended!You will need to be able to learn English.Contact Info: While going through the book, if you have questions about anything, you can reach us at [email protected].**GET YOUR COPY NOW, the price will be 15.99$ soon**

Python for Data Analysis

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491957611
Total Pages : 676 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python for Data Analysis by : Wes McKinney

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 676 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

Data Science from Scratch

Author : Joel Grus
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491904402
Total Pages : 330 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Data Science from Scratch by : Joel Grus

Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Statistical Foundations, Reasoning and Inference

Author : Göran Kauermann
Publisher :
ISBN 13 : 9783030698287
Total Pages : 0 pages
Book Rating : 4.6/5 (982 download)

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Book Synopsis Statistical Foundations, Reasoning and Inference by : Göran Kauermann

Download or read book Statistical Foundations, Reasoning and Inference written by Göran Kauermann and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.