Books For Business Intelligence – Books… this is a remarkable innovation inspired from the 1450s where Johannes Guttenberg invented a new method of printing books on a large scale. Since then books have been a source of knowledge for many, not just priests and monks.
Our continuous learning and development process is always linked to books. Books can serve as food for thought or inspiration. The content of the book, the questions asked by the author and sometimes even the different points of view given can be the cause and start of a new or different idea. Also, even if we are tired at the end of a productive day or when we are enjoying a vacation with our family; Books help to rest and relax.
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In the last decade, we have developed new ways to experience reading. Having physical books in our hands gives us a unique feeling, but this is not the only possibility. Now we can enjoy e-books; when we travel we can take hundreds of them with us. When there is a new release, there is no need to rush to the nearest bookstore – all we need is a Wi-Fi connection to download it directly to our e-library. Audiobooks are another great invention because we can listen to them while traveling, walking or exercising.
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If you want to buy a gift for someone – buy a book or Kindle! There is no limit on book purchases in your Amazon account. Develop a new habit by aiming to read at least one book per month. Learning data analytics is a challenge for beginners. Take your data analysis learning experience a step further with these nine data analysis books. Explore a variety of topics from big data to artificial intelligence.
Data Analytics Books 1. Data Science for Business: What You Need to Know About Data Mining and Data Analytics Thinking by Foster Provost and Tom Fawcett
This book is written by two respected data science experts who introduce their readers to the basic principles of data science and then explore the important role of data in business-related decision making. They do a great job of showing different techniques and ideas related to analytical thinking without being overly technical.
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With this book, you will not only begin to appreciate the importance of communication between entrepreneurs and data scientists, but you will also learn how to approach business problems analytically to create value.
To survive in a data-driven world, we need to acquire the skills necessary to analyze the collected data sets. Data science is important for statistics, data visualization, machine learning, and mathematical modeling. In this book, Steven provides an introductory data science overview for beginners in this growing discipline.
The second half of the book focuses on the essential skills, knowledge and principles needed to collect, analyze and interpret data. This book engages students in a step-by-step guide to developing the ins and outs of theoretical and practical understanding of data science.
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The Data Science Design Manual is a comprehensive instructional guide for students interested in starting their Data Science journey. Finally, Steven adds real-world applications of data science, a wide variety of exercises, Kaggle challenges, and especially, examples from a data science showcase, The Quant Shop, to stimulate the students.
Are you a data enthusiast looking to finally dip your toes into the field? Start of data analysis done by Anil Maheshwari. Think about what data analysis is and how important it is in the real world with this informative, easy-to-follow read.
In fact, this book is considered such an important resource that many universities around the world have added it to their required book lists for their analytics courses. It explains the relationship between business and data by discussing in detail about business intelligence, data mining and data warehousing.
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Written by the lead author of Library Pandas, Python for Data Analysis is a book that explains the basics of manipulating, processing, cleaning and compressing data in Python. This is a hands-on book that guides its readers through an extensive set of real-world case studies and allows them to solve different types of data analysis problems.
It introduces readers to a variety of data science tools in Python to get them started loading, cleaning, transforming, merging and manipulating data. It also walks you through creating educational visualizations in Matplotlib.
5. Big data: a revolution that will change the way we live, work and think by Viktor Mayer-Schönberger and Kenneth Cukier
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This book is tailored for those who want to know the importance of data analytics in various industries. In this work, these two renowned domain experts bring the buzzword “big data” under the spotlight and try to strategize how it affects our world and changes our lives, for better or worse. .
It does not deal with the technical aspects of algorithms or data science applications, but more of a theoretical foundation of what big data really is and how it has become central to various life situations. Apart from encouraging readers to embrace this revolutionary technological advancement, he also reminds them of the potential digital dangers it poses and how we can protect ourselves from it.
This book is great for anyone who wants to read the past, present and future of business intelligence. Explained by Dr. Emphasizing the major successes and overlooking the weaknesses of traditional business intelligence processes, Devlin examines how analytics and big data are changing the modern business intelligence landscape.
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It introduces proven business intelligence practices and explains how the trinity of information, people and processes come together to create competitive advantage and drive business success in this fast-paced world. In addition, in this book, Dr. Delvin with some new models and frameworks that companies and businesses can use for a better tomorrow.
All over the world, culture is visual. Everything we consume from art and advertising to television is visual. Data visualization is the art of telling stories with purpose. In this book, Knaflic highlights the key elements of telling a story that is effectively supported by data. The book goes over the importance of contextualizing your data history, guides you to the most appropriate charts, graphs, and charts to spot trends and outliers, and discusses how to minimize and stay focused on the important points.
This book is a valuable addition to anyone eager to understand the basic concepts of data communication. By the time you finish reading the book, you will have a general understanding of some graphs that add spark to the stories you create from data. Knaflic gives you the knowledge to tell a story with impact.
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Granville drew on his life experience working with big data, business analytics and predictive modeling to write a “handbook” on data science and data scientists. In this book you will find lessons rarely found in traditional statistics, programming or computer science books as the author writes from empirical rather than theoretical knowledge.
In addition, this book contains all the most valuable information to help you succeed in your career as a data scientist. It talks about how data science has evolved in recent times and has become a necessity for organizations that use big data.
This data science book also helps decision makers build a better analytics team by informing them about specialized solutions and their uses. Finally, if you’re looking to launch a data science startup, giving this book a reader will give you a head start with some quick ideas based on Granville’s 20+ years of industry experience.
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Non-technical users are intimidated by programming languages. This book is an asset to any non-technical student of the R language. The author has compiled a list of tools that make accessing statistical models easier. This series of books introduces the reader to R without delving into the details of statistics and data modeling.
The first part of this data science book introduces you to the basics of the R programming language. It consists of data structures, data environments, loop structures and packages. Once you are familiar with the basics, you can start with the second half of the book to learn the steps involved in data analysis such as loading, cleaning and transforming data. The second part of the book provides additional insight to enable exploratory analysis and modeling.
10. Data Analysis: A Comprehensive Beginner’s Guide to Learning the Field of Data Analysis from AZ by Benjamin Smith
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Smith lays out how to learn data analysis from A to Z in easy-to-understand language. The book offers a simplified explanation of challenging topics such as advanced algorithms or even Euclidian Square Estimate. At any point, while reading this book, you will not be overwhelmed by technical jargon or scary formulas.
First, shortly after introducing the topic, the author then explains a real-world use case and then introduces some technical jargon. Smith illustrates almost every practical topic using Python, to allow students to recreate the projects themselves. Practical tips and practical exercises are a bonus.
By implementing Big Data analytics, you explore many ways to research and create
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