The more technology advances in fields such as data analysis, user experience and web development, the more adaptable professionals in tech have to be. Since the tech sector is rapidly evolving, topics that might’ve been innovative a few years ago can quickly become obsolete.
A fast-paced environment like this encourages tech professionals to be lifelong learners and constantly stay up to date with industry trends. This is especially true for those entering the Data Analytics sector of tech. For many people who are new to this sector, getting acquainted with the accelerated environment can be challenging. But a great way of conquering learning curves and challenges within Data Analytics is to immerse yourself in books by top experts. Below we’ve put together our top 10 list of Data Analytics books that are essential to boost your career as a Data Analyst.
[Disclaimer] The links shared below are not affiliated, so we do not receive any commission for selecting these books. They are also not listed in any particular order. With that being said, let’s jump into the books:
1. Data Science For Business: What You Need to Know About Data Mining & Data-Analytic Thinking
If you’ve recently started your journey in the world of data analytics, this book will be a great one for you. It will help you navigate the direction in which you want to grow professionally. Thanks to the lessons of industry experts, Foster Provost and Tom Fawcett, you’ll be able to understand not only how to develop “data-analytic thinking” but also understand “why” advances are taking place in the field of data science. This book will also give you a very comprehensive view of the value of data from a business point of view. From how to collect data efficiently, to how to use data as a competitive advantage to any business, this book is a must-read.
In just 100 pages, Andriy Burkov summarizes the most complex topics and difficult equations in machine learning. Within the information-filled pages he discusses a balance of topics ranging from the math of algorithms, to intuitive visualizations, and brings everything together with easy-to-read explanations. The Hundred-Page Machine Learning Book can be used as inspiration, to check the viability of a project that incorporates machine learning, or to solve somewhat technical problems. Regardless of how you use this book, we’re sure that the Fundamental Algorithms chapter will completely absorb you. So, make sure to have a highlighter handy!
Whether you are taking your first steps in Deep Learning, or want to dive into the field of artificial intelligence, there’s no one better to teach the subject than the creator of the Python library-Keras, François Chollet. We recommend this book for people who have experience with Python and are looking to learn Keras. Throughout the book, Chollet walks you through how to prepare your own learning environment to how to carry out various NLP projects and classification models.
This book by Anil Maheshwari is best for those who have recently begun their journey in the world of data analysis. Among its pages you’ll find thoroughly explained examples taken from the real world. It’s academic text-book structure is ideal for those who want to explore different topics such as Data Warehousing, Data Visualization or Cluster Analysis. The latest version of the book also includes a short tutorial on data-mining with the R programming language. It is one of the most recommended books in universities around the world and it was essential to our book recommendations. We have no doubt that you’ll come back to review its chapters over and over again!
As we mentioned in a previous post, Python is one of the most versatile and powerful programming languages for data analysis. For this reason, it’s essential to know how to program properly to achieve the objectives that you set for your projects. This manual by Vahid Mirjalili and Sebastian Raschka is ideal for this, because it combines theoretical principles of machine learning with several examples and practical cases with Python. In the latest version you’ll find sections that cover the most recent frameworks and libraries, such as scikit-learn, Keras and TensorFlow. A true best-seller!
Considered by many as the 'Bible of AI', this book by Russell Stuart and Norvig Peter, has been present in the curriculum of more than 1,300 universities around 100 countries. This extremely extensive book dives into all the components of Artificial Intelligence. From voice recognition to machine translation, and even autonomous vehicles! If you are curious about the many ways artificial intelligence is part of our lives, then this book will have you hooked. Its 7 chapters will take you from the most basic topics in the field to the most advanced methods!
If Deep Learning catches your attention, then this book by Ian Goodfellow, Yoshua Bengio and Aaron Courville is going to lure you in. With every turn of a page, you’ll be able to dive into a wide range of topics related to deep learning such as mathematical and conceptual background, concepts in linear algebra, probability theory and information theory, numerical computation, and even machine learning. Not to mention that if you are looking for a book that covers neural networks, speech recognition, natural language or algorithm optimization, look no further and get yourself a copy.
Based on their own teaching experience, the authors of this data analysis book have developed a comprehensive guide to learning the main topics that every data scientist should know. To support the concepts that Abu-Mostafa, Magdon-Ismail and Hsuan-Tien Lin teach throughout the book, they also give free access to online e-Chapters that constantly get updated with the current trends in Machine Learning. This book will provide you with very good examples of the application of machine learning in sectors such as finance, engineering, science and business. It’s definitely a must-read!
With the vast background they have behind them, Roger D. Peng and Elizabeth Matsui have developed this manual aimed at those who are new to the world of data in order to share best practices. This book focuses especially on the data analysis process. It covers the most important topics such as data filtering, modeling, analysis techniques, interpretation and communication of results. In addition, the authors also share their personal experiences of the challenges and obstacles they have encountered throughout their long career.
In the field of data analytics, mathematics plays a key role! However, this doesn’t mean that Data Analytics is an inaccessible field, and this book proves it. The purpose of Annalyn Ng and Kenneth Soo is to teach a “gentle” introduction to data science and its algorithms without resorting to mathematics. The concepts are broken down with visual examples and practical cases so that anyone can understand how they work. If you have concerns about how the algorithms of A / B testing, clustering, linear regressions or decision trees work, we think you’ll find this book helpful.
Now that you’ve seen our top 10 must-read data analytics books, it’s time to pick your favorite and dive in! Whether you’re preparing to enroll in Ironhack’s Data Analytics Bootcamp or you’re looking to refresh your knowledge, these books on data analytics will definitely benefit your career.
If you would like to learn more about how we can help you jumpstart a career in Data Analytics and other tech fields, feel free to contact us and schedule a quick chat with one of our learning advisors.
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