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10 Free eBooks Beginners Should Read Before Diving Into Data Science

10 Free eBooks Beginners Should Read Before Diving Into Data Science

Martin F.R.

There is no dearth of books for Data Science which can help get one started and build a career in the field. But before you begin, getting a preliminary overview of these subjects is a wise and crucial thing to do. A healthy dose of eBooks on big data, data science and R programming is a great supplement for aspiring data scientists.

We’ve put together a list of ten eBooks to help you get a holistic perspective about data science and big data. Whether you’re a beginner or advanced, the free eBooks mentioned below can be of a great resource, to begin with:

1|  R for Data Science

Author: By Hadley Wickham and Garrett

What You Learn:

  • You will learn to transform your datasets into a form convenient for analysis
  • You will learn powerful R tools for solving data problems with greater clarity and ease
  • You will learn to examine your data, generate hypotheses, and quickly test them
  • You will learn to provide a low-dimensional summary that captures true “signals” in your dataset
  • You will learn R Markdown for integrating prose, code, and results
  • You will learn  RStudio and the tidy verse, a collection of R packages designed to work

The eBook provides you with a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the complex details.

You can download the free eBook here.

2|  Practical Data Analysis: Second Edition

Author: By Hector Cuesta and Dr Sampath Kumar

What you learn:

  • You will learn to acquire, format, and visualise your data.
  • You will learn to build an image-similarity search engine.
  • You will learn to generate meaningful visualisations anyone can understand.
  • You can learn to get started with analysing social network graphs.
  • You will learn to Install data analysis tools such as Pandas, MongoDB, and Apache Spark.

The eBook provides the necessary understanding of how to Implement machine learning algorithms such as classification or forecasting and how to implement sentiment text analysis also.

You can download the free eBook here.

3|  Learning IPython for Interactive Computing and Data Visualisation

  Author: By Cyrille Rossant

  What you learn:

  • You will learn to load and explore datasets interactively.
  • You will learn to perform complex data manipulations effectively with pandas
  • You will learn to simulate mathematical models with NumPy.
  • You will learn to visualise and process images interactively in the Jupyter Notebook with scikit-image.
  • You will learn to accelerate your code with Numba, Cython, and IPython.parallel and extend the notebook interface with HTML, JavaScript, and D3.

The eBook will provide you with the necessary knowledge to create engaging data visualisations with matplotlib and seaborn.

You can download the free eBook here.

4| Data Mining And Analysis: Fundamental Concepts and Algorithms

Author: By Mohammed J. Zaki and Wagner Meira

What you learn:

  • You learn the fundamental algorithms in data mining and analysis are the basis for big data and analytics, as well as automated methods to analyse patterns and models for all kinds of data.
  • You learn from an algorithmic perspective, integrating concepts from machine learning and statistics, with plenty of examples and exercises.

The eBook covers both fundamental and advanced data mining topics explain the mathematical foundations and the algorithms of data science.

You can download the free eBook here.

5|  A Course in Machine Learning – Third Volume

  Author: By Hal Daume

  What you learn:

  • CIML covers major aspects of modern machine learning.
  • You learn about decision trees, limits of learning, perceptron practical issues, beyond binary classification, linear models, neural networks, kernel and ensemble methods.
  • You will learn aspects of supervised learning, unsupervised learning, large margin methods, probabilistic modelling.

The eBook provides you with broader applications with a rigorous backbone. A subset can be used for beginners interested in a data science career.

You can download the free eBook here.

6 |  The First Encounter with Machine Learning

 Author: By Max Welling

 What you learn :

• R & Data Mining is a set of introductory materials that covers most major aspects of core machine learning.
You learn about Data Representation-processing the Data, Data Visualisation, Types of Machine Learning, Nearest Neighbours Classification.

You will learn about Naive Bayesian Classifier, Naive Bayes Model, Class-Prediction for New Instances, Regularisation

You will learn aspects of supervised learning on a Naive Bayes Classifier.

The eBook provides you with broader concepts on the perceptron model vector regression kernel ridge regression, kernel principal components analysis.

Note: You can download the free eBook here.

7 |  R and Data Mining :

Author: By Yangchang Ziao
 What you learn:

• R and data mining are set of introductory and advanced concepts for both beginners and data miners who are interested in using R

• You learn how to use R for data mining. It presents many examples of various data mining functionalities in R and three case studies of real-world applications.
• You will get to do their data mining research and projects. We assume that readers already have a basic idea of data mining and have some basic experience with R.

The eBook provides you with broader concepts on how to use R to do data mining work in research and applications.

Note: You can download the free eBook here.

8 | Fundamental Numerical Methods and Data Analysis

Author: By George W. Collins

See Also
Advanced SQL

What you learn:

• FNMDA are set of introductory fundamental concepts on numerical methods for linear equations and matrices.

•You learn how to use a polynomial approximation, interpolation and orthogonal polynomials, numerical evaluation of derivatives and integrals, numerical solution of differential and integral equations.

•You will learn the least squares, Fourier analysis and related approximation norms, probability theory and statistics.
The eBook provides you with broader concepts on sampling distributions of moments and statistical tests in data analysis

Note:  You can download the free eBook here.

9 |  Think Stats – Exploratory Data Analysis in Python

Author: By Allen Downey

What you learn:
•Think Stats is an introduction to Probability and Statistics for Python programmers.

•You learn to emphasise simple techniques you can use to explore real data sets and answer interesting questions.

•You learn concepts in probability and statistics with a background in python.

•You will get case studies using data from the National Institutes of Health

The eBook is based on a python library for probability distributions and helps you to work on projects with real data-sets.

Note:  You can download the free eBook here.

10 | Modelling With Data

Author: By Ben Klemens

What you learn:

•You learn to about databases, basic queries, doing more with queries, Joins and subqueries, database design, folding queries into C code.
•You learn concepts in Linear projections principal component analysis, multilevel modelling probability and statistics with a background in python

•You will learn about text processing, shell scripts, some tools for scripting, regular expressions.

The eBook scripting, you how to do hypothesis testing with the CLT, ANOVA, regression.

Note:  You can download the free eBook here.

 You can also read other books covered by us here.

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