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Top 9 ebooks that can level you up in data science


A ton of books for Data Science is available to help you get started with it and build a career you have always dreamt of. Not all of the books are for beginners as some are for data science paragons.

It is always recommended that a beginner should do mind mapping, concerning books that can guide through the basics of data science. What if you get a curated list of books that have been cull out by experts to give beginners a kickstart in data science, big data, Python, R, and so on?

Get ready to get a healthy dose of data science through this post that acknowledges to you top nine eBooks that cover data science in a comprehensive way. The best part is that these books are ideal for freshers and experienced candidates. 

Let’s get started.

1. R for Data Science

Hadley Wickham and Garrett are its authors.

Overview of the book

  • Get a handful of information regarding the molding of datasets into a form convenient for analysis.
  • Know top R tools for solving data problems with finesse.
  • Get tips on your data, develop hypotheses, and test them faster. The book has a brief section of R Markdown-related to code.
  • If you are new to RStudio, this book is worth reading as you will get to know about R packages.

This eBook is an advanced approach to the data science infrastructure, along with tools that can help you streamline complex data science operations.

Click here to get this ebook.

2. Think Stats – Exploratory Data Analysis in Python

Allen Downey is the author of this ebook.

Overview of the book.

  • Want to begin with Python programming? Go for this ebook and know about Probability and Statistics.
  • Get a handful of simple tricks to explore real data sets.
  • From simple to typical examples, after each topic, you will get a ton of questions to test your knowledge.
  • Going through cases studies provided by the National Institutes of Health will verse you in Python.

Though data science has become part of so many programming languages, Python for Data Science is indispensable.

Download this ebook here.

3. Practical Data Analysis

Hector Cuesta and Dr Sampath Kumar have written this ebook.

  • Understand how to gather, edit, and visualize your data.
  • An image-similarity search engine is the core of this ebook.
  • You will be able to develop simple visualizations to make others understand.
  • Learn how to fetch data from social network graphs.
  • Reading this will make you a pro in installing data analysis tools, for example, Apache spark, Pandas, MongoDB, etc.

Plowing through this ebook will let you know how and which machine learning algorithms you should use, and why you need to implement sentiment text analysis with text mining.

Get this free eBook here.

4. Learning IPython for Interactive Computing and Data Visualization

The book is written by Cyrille Rossant.

Overview of the book

  • Learn how to load and get information from datasets easily.
  • Get simple tricks to deal with data manipulations, complex or simple, with pandas.
  • This ebook makes you understand the role of NumPy in mathematical models.
  • Know how sci-kit-image helps you visualize and interpret images precisely in the Jupyter Notebook.
  • Get to know how to Numba, Cython, and IPython.parallel to accelerate your code.

Reading this ebook would help you master matplotlib and seaborn so that you can work on data visualization with ease.

Click here to get this ebook.

5. Data Mining And Analysis: Fundamental Concepts and Algorithms

This ebook is written by Mohammed J. Zaki and Wagner Meira.

Overview of the book

  • Get detailed information of algorithms in data mining and analysis to get acquainted with different spheres of data science. You would even understand automated methods that would enable you to track patterns and different data models.
  • Your algorithmic perspective will become better since this ebook will get you through machine learning and statistics concepts.

Having advanced knowledge of data mining topics is critical in data science. This ebook understands this necessity and therefore covers every data science-oriented concept in detail.

Click here to download the link.

6. A Course in Machine Learning

Hal Daume has written this ebook.

Overview of the book

  • The ebook discusses modern machine learning concepts in detail.
  • You are likely to grow your knowledge base concerning perceptron problems, beyond binary classification, linear models, neural networks, decision trees, limits of learning, and so on.
  • From supervised learning to large margin methods to probabilistic modeling, beginners can dive deep into these technical concepts.

This book can act as a milestone for beginners interested in data science. Moreover, you will know about broader applications that significantly matter to data science.

Click here to get this ebook.

7. R and Data Mining

Yangchang Ziao is the author of this book.

Overview of the book

  • This guide is for those who are new and want to begin fresh with R and data mining. Walking through this ebook will introduce you to differences between R and Python.
  • Apart from understanding R for data mining, you will know about different functionalities of data mining in R. A number of case studies will take you to real-world R examples.
  • Working on any data mining project becomes a lot easier with this ebook. From data manipulation to data interpretation to data validation, the ebook is completely R friendly.

Since R is more about graphical facilities, you will get to know how to display data using R.

Click this link to get your ebook.

Fundamental Numerical Methods and Data Analysis

Written by George W. Collins.

Overview of the book.

  • If you want to stay abreast of numerical methods for linear equations, get this ebook.
  • Whatever mathematical concepts data science involves, such as statistical evaluation of derivatives and integrals, polynomial approximation, interpolation, orthogonal polynomials, etc., the ebook covers everything.
  • The ebook will familiarize you with approximation norms, probability theory, statistics, least squares, Fourier analysis, and so on.

Rather than learning statistical tests in data sampling, you will also understand sampling distributions of moments.

Click here to get this ebook.

Neural networks and Deep learning

This ebook is written by Michael Nielsen.

Overview of the book.

  • This ebook discusses observational data that are critical to computers. What role neural networks play in deep learning, and how they are responsible for advanced machine learning, etc., are some of the points the ebook covers.
  • You will also get to know about speech recognition, image recognition, neural network processing, and so on. These are technical terms that have been explained in most simple ways followed by examples of companies that are actually growing their businesses by integrating machine learning and artificial intelligence with their regular business operations.

Big data analytics has enabled organizations to make sense of their data. Now, they can generate insights into data and predict the nature of their business operations.

Example, Amazon has already turned tables by leveraging artificial intelligence, thus, providing customers with a better shopping experience.

You need to know here that books are mediums for successful careers. Having these ebooks makes sense during certifications and courses.

If these ebooks do not solve the purpose, leveraging online data science forums is an alternative. But you need to focus on understanding concepts rather than skimming over them.