Looking for the best Data Analysis books? Browse our list to find excellent book recommendations on the subject.
1. Storytelling with Data: A Data Visualization Guide for Business Professionals (2015)
Don’t simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and…
2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2017)
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…
3. Microsoft Excel Data Analysis and Business Modeling (5th Edition) (2016)
Master business modeling and analysis techniques with Microsoft Excel 2016, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands on, scenario-focused guide helps you use Excel’s newest tools to ask the right questions and get accurate, actionable answers. This edition adds 150+ new problems with solutions, plus a chapter of basic spreadsheet models to make sure you’re fully up to speed. #38,633 in Books (See Top 100 in Books) #20 in Microsoft Excel Guides #21 in Spreadsheet Books #20 in Database Storage &…
4. Qualitative Data Analysis: A Methods Sourcebook (2019)
Miles, Huberman, and Saldaña’s Qualitative Data Analysis: A Methods Sourcebook is the authoritative text for analyzing and displaying qualitative research data. The Fourth Edition maintains the analytic rigor of previous editions while showcasing a variety of new visual display models for qualitative inquiry. Graphics are added to the now-classic matrix and network illustrations of the original co-authors. Five chapters have been substantially revised, and the appendix’s annotated bibliography includes new titles in research methods. Graduate students…
5. Data Analysis Using SQL and Excel, 2nd Edition (2015)
Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You’ll learn the fundamental techniques before moving into the “where” and “why” of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples…
6. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (2013)
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn…
7. Data Analysis (2017)
Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations…
8. Data Analysis: A Bayesian Tutorial (2006)
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum…
9. Practical Statistics for Data Scientists: 50 Essential Concepts (2017)
Statistical methods are a key part 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…
10. Data Analysis for Continuous School Improvement (2017)
This book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of…
Books shelved as data-analysis: Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists by Philipp K. Janert, Python f Download our free guide on top 15 best books on data analysis! If you found our list of the best data analytics and big data books useful, but your hunger for knowledge hasn’t been satisfied yet, take a look at our best business intelligence books or our data visualization books post to keep growing in your understanding of data science. Free Big Data & Data Management eBooks. Hadoop Explained . Practical Data Analysis. Practical Data Analysis (Second Edition) Creating MySQL Databases. SQL Server Integration Services Using Visual Studio 2005. R Deep Learning Essentials. Free Machine Learning eBooks. Building Machine Learning Systems with Python. What You Need to Know about Machine Learning. Machine Learning with R. Neural A great book, some coffee and the ability to imagine is all one need. Disclaimer: The Picture given below is not the kind of imagination I am talking about. For your convenience, I have divided the answer into two sections: A)Statistics and Probab
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Genesis and download some data analysis books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Big Data: Conceptual Analysis and Applications
Author(s): Michael Z. Zgurovsky, Yuriy P. Zaychenko
Publisher: Springer, Year: 2020, Size: 5 Mb, Download: epub
ID: 2346777
Data Analysis in Bi-partial Perspective: Clustering and Beyond
Author(s): Jan W. Owsiński
Publisher: Springer International Publishing, Year: 2020, Size: 3 Mb, Download: pdf
ID: 2355014
Data Envelopment Analysis with R
Author(s): Farhad Hosseinzadeh Lotfi, Ali Ebrahimnejad, Mohsen Vaez-Ghasemi, Zohreh Moghaddas
Publisher: Springer, Year: 2020, Size: 5 Mb, Download: pdf
ID: 2392893
SPSS Data Analysis for Univariate, Bivariate and Multivariate Statistics
Author(s): Daniel J. Denis
Publisher: Wiley, Year: 2019, Size: 9 Mb, Download: pdf
ID: 2246521
Big Data for Remote Sensing: Visualization, Analysis and Interpretation
Author(s): Nilanjan Dey, Chintan Bhatt, Amira S. Ashour
Publisher: Springer International Publishing, Year: 2019, Size: 6 Mb, Download: pdf
ID: 2251569
Recent Developments in Data Science and Intelligent Analysis of Information
Author(s): Oleg Chertov, Tymofiy Mylovanov, Yuriy Kondratenko, Janusz Kacprzyk, Vladik Kreinovich, Vadim Stefanuk
Publisher: Springer International Publishing, Year: 2019, Size: 26 Mb, Download: pdf
ID: 2252500
Big Data Analysis and Deep Learning Applications
Author(s): Thi Thi Zin, Jerry Chun-Wei Lin
Publisher: Springer Singapore, Year: 2019, Size: 55 Mb, Download: pdf
ID: 2254749
Understanding Least Squares Estimation and Geomatics Data Analysis
Author(s): John Olusegun Ogundare
Publisher: Wiley, Year: 2019, Size: 6 Mb, Download: pdf
ID: 2275473
Please note that this booklist is not definite. Some books are absolutely hot items according to Los Angeles Times, others are composed by unknown authors. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant links you could recommend? Drop a comment if you have any feedback on the list.