With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised ...

Author: Peter Bruce

Publisher: O'Reilly Media

ISBN: 149207294X

Category: Computers

Page: 350

View: 319

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 practical guide--now including examples in Python as well as R--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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning

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 ...

Author: Bri Bruce, Of

Publisher: O'Reilly Media

ISBN: 1491952962

Category: Computers

Page: 250

View: 261

A key component of data science is statistics and machine learning, but only a small proportion of data scientists are actually trained as statisticians. This concise guide illustrates how to apply statistical concepts essential to data science, with advice on how to avoid their misuse. Many courses and books teach basic statistics, but rarely from a data science perspective. And while many data science resources incorporate statistical methods, they typically lack a deep statistical perspective. This quick reference book bridges that gap in an accessible, readable format.

“This book is not another Practical Statistics for Data Scientists 50 Essential Concepts Peter Bruce. OREILLY Practical Statistics for Data Scientists Statistical methods are a key part of data science, yet very few data scientists have ...

Author: Peter Bruce

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491952931

Category: Computers

Page: 317

View: 957

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

Descriptive statistics for multivariate distributions. ... Practical statistics for data scientists: 50 essential concepts. ... Data science: an action plan for expanding the technical areas of the field of statistics.

Author: Asis Kumar Tripathy

Publisher: CRC Press

ISBN: 9781000337884

Category: Computers

Page: 296

View: 966

Cognitive Computing is a new topic which aims to simulate human thought processes using computers that self-learn through data mining, pattern recognition, and natural language processing. This book focuses on the applications of Cognitive Computing in areas like Robotics, Blockchain, Deep Learning, and Wireless Technologies. This book covers the basics of Green Computing, discusses Cognitive Science methodologies in Robotics, Computer Science, Wireless Networks, and Deep Learning. It goes on to present empirical data and research techniques, modelling techniques and offers a data-driven approach to decision making and problem solving. This book is written for researchers, academicians, undergraduate and graduate students, and industry persons who are working on current applications of Cognitive Computing.

A Data Science Approach Vikram Dayal ... Bruce and Bruce (2017) is accessible and written for data scientists. Kennedy (2003) has a useful appendix on sampling ... Practical statistics for data scientists: 50 essential concepts.

Author: Vikram Dayal

Publisher: Springer Nature

ISBN: 9789811520358

Category: Mathematics

Page: 326

View: 682

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.

Author: Laura Igual

Publisher: Springer

ISBN: 3319500163

Category: Computers

Page: 220

View: 266

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing 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 techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; 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.

The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.

Author: Douglas G. Altman

Publisher: CRC Press

ISBN: 0412276305

Category: Mathematics

Page: 630

View: 243

Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research. Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.

N = 50 Use Table B1 to draw a random sample of size n 10 from a population of size N = 70,000 . ... two parts : Using the Tools has straightforward applications to test your mastery of definitions , concepts , and basic computation .

Author: Terry Sincich

Publisher:

ISBN: UCSD:31822026390773

Category: Mathematics

Page: 870

View: 917

This manual includes an Excel primer providing basic instructions on using Windows and Excel. Excel Tutorials appear at the end of pertinent chapters. Self-test questions, key terms, formulas and symbols are included.

Practitioners in these and related fields will find this book perfect for self-study as well.

Author: Steven S. Skiena

Publisher: Springer

ISBN: 3319554433

Category: Computers

Page: 445

View: 198

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze ...

Author: Gary Miner

Publisher: Academic Press

ISBN: 9780123870117

Category: Mathematics

Page: 1000

View: 194

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix