Statistics For Data Science And Analytics
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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
Statistics For Data Science And Analytics
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Author : Peter C. Bruce
language : en
Publisher: John Wiley & Sons
Release Date : 2024-08-06
Statistics For Data Science And Analytics written by Peter C. Bruce and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-06 with Computers categories.
Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.
Introduction To Statistics And Data Analysis
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Author : Christian Heumann
language : en
Publisher: Springer
Release Date : 2017-01-26
Introduction To Statistics And Data Analysis written by Christian Heumann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with Mathematics categories.
This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R as well as supplementary material that will enable the reader to quickly adapt all methods to their own applications.
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
Statistical Foundations Of Data Science
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Author : Jianqing Fan
language : en
Publisher: CRC Press
Release Date : 2020-09-20
Statistical Foundations Of Data Science written by Jianqing Fan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-20 with Mathematics categories.
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
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.
Introduction To Data Science
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Author : Laura Igual
language : en
Publisher: Springer Nature
Release Date : 2024-04-12
Introduction To Data Science written by Laura Igual and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.
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!
Engineering And Management Of Data Science Analytics And Ai Ml Projects
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Author : Manuel Mora
language : en
Publisher: Springer Nature
Release Date :
Engineering And Management Of Data Science Analytics And Ai Ml Projects written by Manuel Mora and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Becoming A Data Head
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Author : Alex J. Gutman
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-13
Becoming A Data Head written by Alex J. Gutman and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-13 with Business & Economics categories.
"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful." Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data - now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.