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**An Introduction to Data Analysis by Tiffany Bergin**

Title |
An Introduction to Data Analysis |

Author |
Tiffany Bergin |

Publisher |
SAGE |

Release |
2018-10-15 |

Category |
Reference |

Total Pages |
296 |

ISBN |
1526452316 |

Language |
English, Spanish, and French |

**Book Summary:**

Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques - Data management - The big data phenomenon - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.

**An Introduction to Data Analysis in R by Alfonso Zamora Saiz**

Title |
An Introduction to Data Analysis in R |

Author |
Alfonso Zamora Saiz |

Publisher |
Springer |

Release |
2020-09-24 |

Category |
Computers |

Total Pages |
276 |

ISBN |
9783030489960 |

Language |
English, Spanish, and French |

**Book Summary:**

This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.

**A General Introduction to Data Analytics by João Moreira**

Title |
A General Introduction to Data Analytics |

Author |
João Moreira |

Publisher |
John Wiley & Sons |

Release |
2018-06-25 |

Category |
Mathematics |

Total Pages |
352 |

ISBN |
1119296269 |

Language |
English, Spanish, and French |

**Book Summary:**

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

**Introduction to Statistics and Data Analysis by Christian Heumann**

Title |
Introduction to Statistics and Data Analysis |

Author |
Christian Heumann |

Publisher |
Springer |

Release |
2017-01-26 |

Category |
Mathematics |

Total Pages |
456 |

ISBN |
3319461621 |

Language |
English, Spanish, and French |

**Book Summary:**

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.

**An Introduction to Statistical Methods and Data Analysis by R. Lyman Ott**

Title |
An Introduction to Statistical Methods and Data Analysis |

Author |
R. Lyman Ott |

Publisher |
Cengage Learning |

Release |
2008-12-30 |

Category |
Mathematics |

Total Pages |
1296 |

ISBN |
9780495017585 |

Language |
English, Spanish, and French |

**Book Summary:**

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

**Introduction to Data Science by Laura Igual**

Title |
Introduction to Data Science |

Author |
Laura Igual |

Publisher |
Springer |

Release |
2017-02-26 |

Category |
Computers |

Total Pages |
220 |

ISBN |
9783319500164 |

Language |
English, Spanish, and French |

**Book Summary:**

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.

**An Introduction to Statistics and Data Analysis Using Stata by Lisa Daniels**

Title |
An Introduction to Statistics and Data Analysis Using Stata |

Author |
Lisa Daniels |

Publisher |
SAGE Publications |

Release |
2019-01-11 |

Category |
Social Science |

Total Pages |
392 |

ISBN |
1506371825 |

Language |
English, Spanish, and French |

**Book Summary:**

An Introduction to Statistics and Data Analysis Using Stata® by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.

**An Introduction to Data Analysis by Bruce D. Bowen**

Title |
An Introduction to Data Analysis |

Author |
Bruce D. Bowen |

Publisher |
W.H. Freeman |

Release |
1980 |

Category |
Mathematical statistics |

Total Pages |
213 |

ISBN |
9780716711742 |

Language |
English, Spanish, and French |

**Book Summary:**

**An Introduction to Statistical Genetic Data Analysis by Melinda C. Mills**

Title |
An Introduction to Statistical Genetic Data Analysis |

Author |
Melinda C. Mills |

Publisher |
MIT Press |

Release |
2020-02-18 |

Category |
Science |

Total Pages |
432 |

ISBN |
0262538385 |

Language |
English, Spanish, and French |

**Book Summary:**

A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website.

Title |
An Introduction to Statistical Methods and Data Analysis |

Author |
R. Ott |

Publisher |
Cengage Learning |

Release |
2015-06-11 |

Category |
Mathematics |

Total Pages |
1296 |

ISBN |
9781305269477 |

Language |
English, Spanish, and French |

**Book Summary:**

Ott and Longnecker’s AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.