Top 10 free data science eBooks to commence learning on the trending topic of data science. Data science is the buzzword amongst enthusiasts, new beginners, and experienced professionals. These expert-written eBooks will be handy in getting started on data science and gaining insight into big data.
In early 2017 the term ‘Data Science’ was virtually unknown, but by mid-2018 it was being widely touted as the latest trend, with all the usual hype. Like ‘cloud computing’ before it, the term has today been adopted by everyone, from product vendors to large-scale outsourcing and cloud service providers keen to promote their offerings. But what really is Data Science?
The question can be answered with the help of a quick web-search or text-books, ultimately. However, it can be overwhelming for a beginner to select what textbook to grab out of the hundreds available in the present date, for the said topic. Top 10 free data science ebooks we have selected for beginners and experienced professionals in the list. Many covers hands-on approach and are suitable for individuals who want to begin with a scratch.
1. Neural Networks and Deep Learning
In Michael Nielsen’s authored Neural Networks and Deep Learning, programming paradigms are focused on. It covers an introduction to data learning. One of the use cases includes recognizing handwritten digits and using them to build neural networks. Other basic use cases are featured in this book too. The book promises an understanding of core concepts of neural networks, and how they function. Hands-on is provided in the book. Mathematical understanding required to grab concepts is modest and several mathematical concepts are mentioned wherever required.
Get the book here.
2. Think Bayes
Think Bayes authored by Allen B. Downey teaches Bayesian statistics using computational methods. Basic programming skills & understanding is a prerequisite for starting this book. At the end of the book, the learner will be confident in making modeling decisions (which model to select) without the ifs and buts. Examples graduate from simple to real-world applications.
Get the eBook here
3. Statistical Learning with Sparsity: The Lasso and Generalizations
Statistical Learning with Sparsity: The Lasso and Generalizations are co-authored by Trevor Hastie, Robert Tibshirani, and Martin Wainwright. The book emphasizes on statistical learning, with sparsity, to extract meaningful patterns from large datasets. Data flow is understood with interesting examples of sports like basketball. The medical field’s data flow is also understood and used as an instance in the book. Statistical concepts are mentioned frequently throughout the book, which will be useful for readers with no prior knowledge of statistics.
Get the book here
4. The Field Guide of Data Science
The Field Guide of Data Science covers methods to view data under the lens of meaningful assets. It’s more adaptable for employed individuals as it aims to teach the reader about transforming data flow throughout the organization. It still is suitable for beginners by showcasing around tips and tricks, toolboxes and real-life applications. Case studies include examples of leading airlines.
Get the book here.
5. The White Book of Data
This book is also more suited for employed individuals like the previously mentioned book. Business analytics isn’t the same as data science, first things first. However, this book will be guiding around business operations and analytics. The learning grows while learning about big data first. It is still suited for beginners as it progresses from introducing concepts of data types, i.e from a scratch.
Get the book here.
6. Machine Learning
As the name suggests, this book discusses machine learning, it’s the origin, uses, languages involved and history, briefly. It is a hands-on approach based narrative, more suited for developers oriented approach. It also talks about many ways by which one can view and make use of data available, while not strictly adhering to big data concepts.
Get the book here.
7. Beginners Guide to Analytics
Across multiple industries like medicine and sports, this book provides examples and guiding light. The language is simple and the book is a brief reading. It also discusses career aspects in analytics while discussing all the buzz and keywords related to data science. This book is for a side-table read, and extra knowledge on the subject matter.
Get the book here.
8. Data Science: Theories, Models, Algorithms, and Analytics
Theories, data science algorithms, tools, and analytics are discussed in this eBook authored by Sanjiv Ranjan Das. The book covers in detail statistics, modeling, artificial neural networks, analysis techniques, as well as clustering. The book makes clear definitions of core concepts, makes mathematical definitions available in the same chapters wherever needed. It is a good read for beginners as well as for someone with prior knowledge to refresh basic theories.
Get the book here.
9. Automating Boring Stuff with Python
The book is with a hands-on approach using Python language. It is well suited for beginners. As the simple examples are done with, the reader can get into more useful real-life programs like filling out online forms or making automated email reminders. The 20 chapters of the book are well narrated, witty and easy to follow so the reader isn’t easily bored into the new learning experience.
Get the book here.
10. An Introduction to Statistical Learning
Statistics can’t be taken out of learning data science. Regardless of the personal Vikings of statistical modeling, the book somehow pulls the reader into interest with a learning approach. This comes by, as the book is penned down by authors who have years of experience in the said subject matter. The applications discussed are in the R language. The book discusses the classification, regression, sampling and other well-known topics/ concepts of the statistical domain. It will be handy for learning statistics and then into a deep dive in data science.
Get the book here.
This summarises the Top 10 free data science eBooks to get started with artificial intelligence. Also, check out ways to minimize coding errors once you start hands-on with data science.