Introduction to Data Science

Data science is a multifaceted discipline that incorporates skills from various fields such as computer science, mathematics, statistics, and machine learning. For those new to data science, determining where to begin can be overwhelming. While formal education options like degree programs and boot camps provide structured learning paths, they often come with hefty price tags. Fortunately, you can create a personalized learning experience using free or low-cost materials.

In a previous article, I outlined how to develop your own tailored curriculum for mastering data science, which you can find here. The internet is rich with free resources, yet identifying the most effective ones can be challenging. Books are an excellent resource for beginners, and there are several that you can access at no cost, covering fundamental data science concepts.

This article introduces six of my top free book recommendations for beginners, organized in the order I suggest you tackle them:

1. Fundamentals of Python Programming
2. Statistics for Data Science
3. Pandas for Data Analysis
4. The Python Data Science Toolkit
5. Machine Learning
6. Deep Learning

1. Python Programming Made Easy

By Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers

This book, "How to Think Like a Computer Scientist," is regarded as one of the best introductory texts for learning Python. It serves as a foundational resource, ideal for beginners who have no prior programming experience. The text covers essential coding principles, and each chapter is complemented by a YouTube video that elaborates on the key concepts. Additionally, the authors provide a set of hands-on tutorials featuring over 15 practical Python projects for you to apply what you've learned.

2. Practical Statistics for Data Science

By Peter Bruce & Andrew Bruce

This book is an exceptional resource for grasping the vital statistical concepts relevant to data science. It covers a wide array of topics, including data sampling, distributions, statistical experiments, and machine learning, all explained in accessible language. It also features practical code examples utilizing both R and Python.

3. Pandas: The Go-To Data Analysis Tool

By Wes McKinney and the Pandas Development Team

Pandas is the primary Python library used for data exploration, cleaning, and processing. This guide provides thorough instructions on installation, basic usage, plotting, and data analysis. It assumes some prior knowledge of Python, making it essential to first explore "Python Programming Made Easy," but it stands as a valuable reference for both beginners and more advanced learners.

4. Hands-On Machine Learning with Scikit-learn, Keras, and TensorFlow

By Aurelien Geron

This book offers a comprehensive and practical introduction to machine learning, focusing on popular Python libraries such as Scikit-learn, Keras, and TensorFlow. It effectively covers essential theoretical concepts of machine learning, including different system types and common algorithms. The book includes a complete walkthrough of a typical machine learning project, making it an excellent resource for newcomers.

5. Python Data Science Handbook

By Jake VanderPlas

This handbook serves as a broad introduction to the tools available in the Python data science ecosystem. It covers foundational topics such as the NumPy library, including arrays, operations on arrays, and data types in Python. Moreover, it provides a beginner-friendly overview of data analysis using Pandas and machine learning with Scikit-learn.

6. Deep Learning for Coders with Fastai and PyTorch

By Jeremy Howard and Sylvain Gugger

This book offers a practical introduction to deep learning for those familiar with Python programming. While it does not require extensive knowledge of mathematics or statistics, it provides clear explanations of deep learning concepts. It is an ideal starting point for anyone looking to develop their first deep learning models.

Conclusion

The internet is filled with an array of free or low-cost resources for learning data science, including courses, lectures, tutorials, videos, blogs, and books. In this article, I've highlighted six free books that offer a solid foundation for beginners in data science. For those interested in expanding beyond these introductory concepts, I have previously compiled several articles featuring a variety of additional free resources for learning these subjects.

• How to Learn Data Science for Free

  A comprehensive guide featuring resources at no cost.

• 10 Free Resources for Learning Natural Language Processing

  Explore NLP with these excellent free materials.

• Data Science Learning Resources

  A curated list of free materials for studying data science.

Thank you for reading!