Looking for the best Data Analysis books? Browse our list to find excellent book recommendations on the subject.
- Storytelling with Data: A Data Visualization Guide for Business Professionals (2015)
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2017)
- Microsoft Excel Data Analysis and Business Modeling (5th Edition) (2016)
- Qualitative Data Analysis: A Methods Sourcebook (2019)
- Data Analysis Using SQL and Excel, 2nd Edition (2015)
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (2013)
- Data Analysis (2017)
- Data Analysis: A Bayesian Tutorial (2006)
- Practical Statistics for Data Scientists: 50 Essential Concepts (2017)
- Data Analysis for Continuous School Improvement (2017)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Best Data Analysis Books to Read
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 Library 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.
Python for Geospatial Data Analysis
Author(s): Bonny P. McClain
ID: 3061514, Publisher: O'Reilly Media, Inc., Year: 2022, Size: 25 Mb, Format: epub
The Statistical Analysis of Doubly Truncated Data : With Applications in R
Author(s): Prof Carla Moreira, Jacobo de Uña-Álvarez, Rosa M. Crujeiras
ID: 3132420, Publisher: Wiley, Year: 2022, Size: 4 Mb, Format: pdf
Data Analysis with Python and PySpark
Author(s): Jonathan Rioux
ID: 3146132, Publisher: Manning, Year: 2022, Size: 3 Mb, Format: epub
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.