Here are 100 books that Be Data Literate fans have personally recommended if you like
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I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
This is a foundational book on analytics and data science as a business function and helped to shape the development of the practice. It provides a view of the discipline through a business lens and avoids deep technical examinations. Though much has changed in the 15 years since it was originally published, it is still essential reading for a leader in the field. No book since has captured as well the competitive differentiation that analytics provides.
You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new…
It is April 1st, 2038. Day 60 of China's blockade of the rebel island of Taiwan.
The US government has agreed to provide Taiwan with a weapons system so advanced that it can disrupt the balance of power in the region. But what pilot would be crazy enough to run…
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
Data scientists and analytics specialists are great at building models and algorithms, but often wrap them in a presentation or dashboard that diminishes their value and reduces the likelihood of their work being adopted. This book encourages practitioners to always consider the last mile and to pay as much attention to presentation and aesthetics as we do to the model itself.
Master the art and science of data storytelling-with frameworks and techniques to help you craft compelling stories with data.
The ability to effectively communicate with data is no longer a luxury in today's economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative-to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories.
Narratives are more powerful than raw statistics, more enduring than pretty charts. When…
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
Since data science is, at its core, people helping people make decisions, it is essential that we can establish productive relationships with our stakeholders. This is a skill that needs to be given the same level of effort as we give to coding or statistics. Gilbert’s book is a great resource to help technically oriented people to advance their people skills.
"For the engineer, scientist, or technology professional seeking to communicate better in the business world, this is the book you've been craving your entire career!" ” — Douglas Laney, Innovation Fellow, West Monroe, and best-selling author of "Infonomics"
Your analytical skills are incredibly valuable. However, rational thinking alone isn’t enough.
Have you ever:
Presented an idea, but then no one seemed to care?
Explained your analysis, only to leave your colleague confused?
Struggled to work with people who are less analytical and more emotional?
In these situations, people skills make the difference, and research shows these skills are becoming increasingly…
A Duke with rigid opinions, a Lady whose beliefs conflict with his, a long disputed parcel of land, a conniving neighbour, a desperate collaboration, a failure of trust, a love found despite it all.
Alexander Cavendish, Duke of Ravensworth, returned from war to find that his father and brother had…
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
Management as a skill is typically established and honed by osmosis, mimicry, and corporate crash courses. Data scientists pursuing management roles need to understand management from base principles to create meaningful change and establish productive team conventions. After almost 70 years, Drucker’s book still stands up as a foundational piece of reading.
A classic since its publication in 1954, The Practice of Management was the first book to look at management as a whole and being a manager as a separate responsibility. The Practice of Management created the discipline of modern management practices. Readable, fundamental, and basic, it remains an essential book for students, aspiring managers, and seasoned professionals.
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Apache Spark has a very high point of entry for newcomers to the Big Data ecosystem.
However, it is a key tool that almost everyone is using for running distributed processing. I recommend everyone to read this book before delving into production solutions based on Apache Spark.
This book will allow you to alleviate many spark problems, such as serialization, memory utilization, and parallelization of processing.
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for…
I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.
For those intending to use R with an eye on the popular 'Tidyverse' suite of packages – which facilitate the handling, manipulation, and visualisation of data sets – it's hard to go past this book. From the founding contributors of the RStudio/Tidyverse worlds, this is a great way to learn about this dialect of R against the overarching backdrop of statistical data analysis and data science.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along…
The Duke's Christmas Redemption
by
Arietta Richmond,
A Duke who has rejected love, a Lady who dreams of a love match, an arranged marriage, a house full of secrets, a most unneighborly neighbor, a plot to destroy reputations, an unexpected love that redeems it all.
Lady Charlotte Wyndham, given in an arranged marriage to a man she…
I’m a journalist and a tinkerer. I’m fascinated not only by how things work but by how small levers can move mountains. Growing up in the workshop of my grandfather, an old Boston boatwright, I was mesmerized by the idea that a small rudder could maneuver a huge vessel. In college, I fell in love with how a small idea or expression could redirect a course of research or a country. As a self-taught maker of things, I appreciate how technologies empower us. I’ve chosen these books because they’re examples of how small ideas become things, lines of research, or patterns of thinking that shift human progress in unknowable ways.
I love gutsy books by outsiders, and Ms. Saxena, as a woman of color working in the Ivy League and the worlds of artificial intelligence and Big Data, is very much an outsider.
That makes her deep knowledge and insights into how AI and Big Data are changing business even that much more interesting. Plus, this is one of the only books I’ve read that explains how artificial intelligence works in a clear, direct way that doesn’t assume the reader already knows about things like machine learning and neural nets.
Have you heard about artificial intelligence (AI) and big data but felt they are technologies too big or too complicated for you or your business? Do you imagine AI as a Hollywood science fiction stereotype or something in the far and distant future?
Take heart. AI is none of those things. It's part of our everyday lives, and it has the power to transform your business.
This book will put AI, big data, the cloud, robotics, and smart devices in context. It will reveal how these technologies can dramatically multiply any businesses-including yours-by strategically using your data's latent, transformative potential.…
I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations
A lightly technical introduction to a comprehensive framework defining and evaluating the quality of information generated by statistical analysis. It expands the role of analytics by including dimensions that affect information quality such as data resolution, data integration, operationalization, and generalizability of findings. This wide-angle perspective provides a practical checklist that has been found useful in applications. Multiple case studies enable the reader to connect to his favorite topic, but also learn from other areas.
Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application…
This book follows the journey of a writer in search of wisdom as he narrates encounters with 12 distinguished American men over 80, including Paul Volcker, the former head of the Federal Reserve, and Denton Cooley, the world’s most famous heart surgeon.
In these and other intimate conversations, the book…
I am Wes McKinney, creator of the Python pandas project and author of Python for Data Analysis. I have been using Python for data work since 2007 and have worked extensively in the open source community to build accessible and fast data processing tools for Python programmers.
This is a great follow-up book to Python Data Science Handbook.
Co-authored by one of the core developers of scikit-learn, this provides a deeper introduction to doing machine learning work in Python. This will give you a solid foundation to be able to move on later to deeper topics including deep learning or other AI topics.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the…
