Top 10 Data Science Books to Read in 2023

Rapid digitisation and dependency on the internet are the causing agents of many data professionals are struggling to manage and use. The evolving field of data science is well-equipped to keep up with enormous data production and reap value out of it. 

Data science refers to the study of managing piles of produced data through in-depth analysis, structuring, and integration into valuable insights through machine learning tools and visualisation methods. It uses algorithms to build predictive models and implement them on unstructured data to redeem information. 

Following the innovation and significance of data science, data science is set to be the most in-demand job sector. It is also expected to expand its market size to USD 322.9 billion by 2026. So, why not kick-start your data science career now?

Our recommendations for the top ten best data science books for beginners are essential to go through in your journey to land a data science job!

Top Data Science Books

1. Data Science for Dummies by Lillian Pierson

As the demand for data science professionals grows, more and more people are picking up data science courses to seek a position in the sector with massive future growth. This book is one of the fundamental resources used by people to nurture their data science basics. 

Data science for dummies is a quick start for people getting into the course and seeking a mix of fundamentals and advanced concepts. The book covers the assimilation of data science, big data, and data engineering to recognise the implemented technology for effective data management. Beginners can pick this one for a headstart in data science. 

2. Head First Statistics by Dawn Griffiths

Head First Statistics is an excellent introduction to data science and its statistical aspects comprising probability, regression, correlation, and inferential statistics lessons. Just like other Head First series, the book prioritises keeping the book conversational and reader-friendly. Hence, it’s preferred by many people as their first insight into data science. 

To keep the book comprehensive, usage of graphics and diverse real-life examples are included for superior clarity and implementation of topics. Head First Statistics is everything a beginner can wish for in their data science beginner resource for an easy and informative start. 

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3. Data Science from Scratch: First Principles with Python by Joel Gurus

Data science from scratch targets the core structures of data science and makes it comprehensive for readers- Statistics and Mathematics. It works along with the most basic data science tools and narrates their implementation from scratch with an explanation of the principles behind these tools. 

Joel Gurus also emphasises hacking skills essential for data scientists through this book while extending the opportunity to learn more about natural processing and network analysis to widen your data science knowledge. In brief, it contains every aspect of data science in bite-sized pieces for a quick narration of what’s to be expected from the data science course.

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4. Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce

Practical Statistics for Data Scientists is a great start for aspiring data scientists hoping to find an overview of data science covering sampling, randomisation, distribution, and other high-level concepts. 

The book contains surveys of ML models, advanced concepts, and thorough explanations of why these concepts are relevant to data science. While the explanations and examples are not very detailed, the book can work as an easy reference for quick concept revision. 

5. Python Data Science Handbook by Jake VanderPlas

The book emphasises data science concepts through Python, which often helps as a powerful tool to work with many natural language processing algorithms. The handbook narrates concepts of NumPy, IPython, and Jupyter to implement a computational environment suitable to Python using data scientists.

 The Python handbook might not suit a data science novice. But, professional programmers aiming to step into machine learning through Python can opt for this book.

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6. Storytelling with Data by Cole Nussbaumer Knaflic

Obtaining valuable data is not the only aspect of data science, and this book highlights one of the essential aspects of data science: data visualisation. The book follows a storytelling pattern and extensive graphics to explain diverse concepts. 

The author has tried to keep the book as comprehensive as possible to allow users to dive into essential points rather than beating around the bush. Yet, reading through the book prepares candidates to take on critical concepts such as observation, analysis of important information, visualisation tools, etc. 

7. An Introduction to Statistical Learning by Trevor Hastie, Gareth James, Robert Tibshira, and Daniela Witten

The book contains an in-depth understanding of the tools required to deal with complex data science structures written by a group of experts. Each chapter provides a helpful modelling and prediction technique for the relevant applications. 

It presents real-world examples to keep relatable while extending diverse analysis implementation methods to simplify the process for readers with easy application.

8. R for Data Science by Hardley Wickham

The book is an excellent combination of fundamental and advanced data science concepts. The initial stages will introduce readers to the basic concepts, and as one continues to proceed further in the chapter, the concepts turn complex. 

R for Data Science compiles concepts and the causing reason behind their implementation for a thorough understanding. It also covers data science’s practical business aspects, which is pretty relevant in the current data science sector. 

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9. Doing Data Science by Cathy O’Neil

This book is an excellent introduction to data science with the presence of reliable methods, models, and algorithms extending their correct implementation. It explores topics like logistic regression, algorithms, statistical inference, data engineering, and data visualisation for a significant command of core data science concepts. 

Learners can include this in their data science study resources to understand data science concepts better.

10. Pattern Recognition and Machine Learning by Christopher Bishop

Pattern Recognition and Machine Learning is a must-have book for data science aspirants of all levels. The book provides in-depth insight into data science concepts through extensive usage of coloured images and graphs for engaging readers. 

It thoroughly explains machine learning concepts and their analytical features through detailed analysis. It is an exceptional start to your data science career.

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The program covers essential prerequisites, fundamentals, and advanced data science concepts, concluded by real-time Capstone projects for a balanced theoretical and practical compilation of data science skills. upGrad’s extended support through expert mentorship, career guidance, and student support are a few other features that make this program superior to any other available. 

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Conclusion

Extensive data science course structure can burden learners through elaborate and hectic course schedules. Opting for the right learning material can simplify your data science learning journey. These are some of the finest data science books for beginners available, containing fundamental data science concepts. While learners get pumped to pick the advanced editions to gain proficiency faster, it is vital to understand your skill level and grab learning resources.