Top 7 Data Science Books to Transform You from Novice to Intermediate

Breaking into the world of data science is no easy feat. As an interdisciplinary field combining coding, math, statistics, and domain expertise, data science has a steep learning curve. Aspiring data scientists can easily get overwhelmed by the sheer breadth of skills they need to master.

One common pitfall is jumping between random online tutorials and courses, which leads to piecemeal knowledge without a strong foundation. A much more effective approach is finding a few authoritative books and systematically studying them from start to finish.

High-quality data science books provide in-depth, comprehensive coverage of the core concepts and techniques. They give you a structured learning path curated by experts. And they force you to engage with the material more deeply than skimming a blog post.

Demand for data science books has exploded in recent years, with the global data science platform market expected to reach $25.94 billion by 2027, at a CAGR of 26.9% from 2020-2027 according to Analytics Insight[^1^]. To help you navigate this booming market, here are 7 of the best data science books to take you from novice to intermediate.

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

• Publisher: O‘Reilly Media
• Authors: Aurélien Géron
• Publication Date: October 2019 (2nd Edition)
• Pages: 856

Aurélien Géron‘s "Hands-On Machine Learning" is widely considered the bible of applied ML. Newly updated for TensorFlow 2 and the latest Scikit-Learn release, this bestseller is unrivaled in its scope, clarity, and practicality.

The book shines in its breadth and depth of coverage. Part I is a comprehensive guide to classical machine learning with Scikit-Learn, spanning everything from basic regression to advanced techniques like ensemble methods and dimensionality reduction.

Part II provides a full deep learning curriculum using Keras and TensorFlow 2, starting from first principles of neural networks all the way to advanced architectures like autoencoders and GANs. Importantly, it actually explains the theory and math – not just the code.

As the title suggests, the book takes a hands-on approach with plenty of code examples. But it doesn‘t neglect the fundamentals. You‘ll come away with a rock-solid foundation in both classical machine learning and deep learning. It‘s as close to a one-stop shop for applied ML mastery as you‘ll find.

2. Python for Data Analysis

• Publisher: O‘Reilly Media
• Author: Wes McKinney
• Publication Date: October 2017 (2nd Edition)
• Pages: 550

To do data science effectively, you absolutely must be proficient with data wrangling, manipulation, and visualization. Wes McKinney‘s "Python for Data Analysis" is the definitive guide to these essential skills in Python.

Written by the creator of the pandas library, this book offers comprehensive coverage of the entire data analysis process in Python. It starts with the basics of NumPy and gradually builds up to advanced data manipulation with pandas, including time series, grouping, merging, joining, and reshaping data.

The book shines in teaching data cleaning, preparation, and visualization with pandas, matplotlib, and seaborn. It‘s full of practical examples and real-world datasets. After working through it, you‘ll be able to slice and dice data in Python with the best of them.

While not a machine learning book per se, these data wrangling skills are table stakes for any data scientist. McKinney estimates that data cleaning and preparation account for up to 80% of the work of data scientists[^2^]. This book helps you do that 80% efficiently and effectively.

3. Data Science from Scratch

• Publisher: O‘Reilly Media
• Author: Joel Grus
• Publication Date: May 2019 (2nd Edition)
• Pages: 406

Subtitled "First Principles with Python", this book delivers exactly that – a ground-up introduction to data science using only Python and its built-in libraries. No pandas, NumPy, or Scikit-Learn here. The goal is to deeply understand data science from first principles by implementing everything from scratch.

Joel Grus, a former Google software engineer and Kaggle competitor, guides you through the fundamentals of data science, including data visualization, statistics, probability, linear algebra, and machine learning. Hands-on examples and exercises in every chapter cement the concepts.

While rebuilding NumPy and pandas functions from scratch is not the most efficient approach, it provides an invaluable learning experience. The DIY ethos gives you a deeper appreciation for how data science libraries actually work under the hood.

This book is ideal for Python-proficient beginners seeking a ground-up data science education. Be warned: it requires more math and coding than most introductory books. But if you put in the work, you‘ll come away with an exceptional foundation.

4. Practical Statistics for Data Scientists

• Publisher: O‘Reilly Media
• Authors: Peter Bruce, Andrew Bruce, Peter Gedeck
• Publication Date: June 2020 (2nd Edition)
• Pages: 368

Too many aspiring data scientists fail to recognize that statistics is the backbone of data science. They get enamored with fancy machine learning models, but lack the statistical chops to use them correctly and interpret the results. This book aims to fix that.

"Practical Statistics for Data Scientists" focuses on teaching statistical methods and concepts that are directly applicable to data science. Written in a conversational style with plenty of real-world examples, it avoids tedious mathematical proofs in favor of intuitive explanations.

The book covers a wide range of statistical topics, including sampling, hypothesis testing, regression, Bayesian methods, and statistical machine learning. Code examples are provided in both Python and R. Unlike many stats textbooks, it‘s specifically geared towards the unique concerns of data scientists.

By demystifying statistics and making it approachable, this book equips data scientists to think statistically and avoid common pitfalls. As the famous statistician John Tukey said, "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data." This book helps ensure your analysis is statistically sound.

5. Fundamentals of Data Visualization

• Publisher: O‘Reilly Media
• Author: Claus O. Wilke
• Publication Date: November 2019
• Pages: 390

Data visualization is both an art and a science. While there are lots of tools that make plotting easy, far fewer resources teach how to make truly effective visualizations that accurately convey insights. "Fundamentals of Data Visualization" fills that gap masterfully.

The book‘s author, Dr. Claus O. Wilke, is a professor of integrative biology and statistics. He brings an academic rigor to the material while keeping it engaging and accessible. Rather than focus on specific plotting libraries, the book teaches the principles of perception, design, color theory, and visual communication as they relate to data viz.

Wilke breaks down the various roles of data visualization, from exploration and discovery to teaching concepts and supporting arguments. He provides a framework for thinking critically about visualization choices and identifying what works and what doesn‘t. The book is packed with examples of good and bad visualizations along with clear explanations of the underlying factors.

While not a coding book per se, it does provide ggplot code to recreate the hundreds of visualizations. The real value, though, is in the lucid explanations of visualization principles and best practices. After reading it, you‘ll never look at a chart or dashboard the same way again.

6. Data Science at the Command Line

• Publisher: O‘Reilly Media
• Author: Jeroen Janssens
• Publication Date: October 2021 (2nd Edition)
• Pages: 272

For data scientists coming from a non-programming background, the Unix command line can seem arcane and intimidating. This unique book aims to show that the command line can actually be a powerful ally for data science workflows.

Author Jeroen Janssens, a senior data scientist at a major Dutch bank, demonstrates how a plethora of command line tools can be combined to do everything from simple data wrangling to advanced machine learning. While the book focuses on macOS and Linux, Windows users can follow along using the Windows Subsystem for Linux.

The book strikes a great balance between introductory material, like navigating the command line and parsing files, and more advanced topics, like creating reusable command line tools, managing data workflows, and even training machine learning models. Janssens keeps the tone light and engaging, peppering in nerdy humor and entertaining examples.

In the end, you‘ll gain a whole new appreciation for the command line and a powerful set of tools to add to your data science workflow. While not appropriate for complete newcomers to data science, those with some familiarity will find it an eye-opening resource.

7. Machine Learning with PyTorch and Scikit-Learn

• Publisher: Packt
• Authors: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
• Publication Date: February 2022
• Pages: 770

PyTorch has quickly become the leading deep learning framework for research and is increasingly being adopted in industry. However, learning it has long required wading through scattered documentation and tutorials. "Machine Learning with PyTorch and Scikit-Learn" is positioned to become the definitive guide to PyTorch, filling the need for a single resource covering both the basics and advanced material.

The trio of authors, led by Dr. Sebastian Raschka, are some of the foremost experts on PyTorch and have created a monumental work. The book‘s 770 pages are split into four parts:

1. Giving a thorough grounding in machine learning basics with Scikit-Learn
2. Introducing neural networks and deep learning with PyTorch
3. Providing a comprehensive deep learning reference with PyTorch
4. Covering advanced topics like generative models, reinforcement learning, and best practices.

The book shines for its crystal clear explanations of the core concepts, well-thought-out pedagogical approach, and plentiful interactive Jupyter notebook examples. It manages to make the theory digestible while sacrificing none of the mathematical rigor.

With "Machine Learning with PyTorch and Scikit-Learn," Raschka and his co-authors have created what will likely become the standard PyTorch textbook. Whether you‘re looking to learn PyTorch or level up your deep learning game, this book has you covered.

Comparison Table

Book Title	Publisher	Authors	Publication Date	Page Count	Key Topics	Unique Features
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow	O‘Reilly Media	Aurélien Géron	October 2019	856	Classical ML, Deep Learning	Complete applied ML resource
Python for Data Analysis	O‘Reilly Media	Wes McKinney	October 2017	550	Data manipulation, visualization	Comprehensive pandas coverage
Data Science from Scratch	O‘Reilly Media	Joel Grus	May 2019	406	Data science fundamentals	Implements ML from scratch
Practical Statistics for Data Scientists	O‘Reilly Media	Bruce, Bruce, Gedeck	June 2020	368	Statistical methods	Geared for data scientists
Fundamentals of Data Visualization	O‘Reilly Media	Claus O. Wilke	November 2019	390	Visualization principles	Academic rigor, engaging style
Data Science at the Command Line	O‘Reilly Media	Jeroen Janssens	October 2021	272	Command line tools	Unique command line focus
Machine Learning with PyTorch and Scikit-Learn	Packt	Raschka, Liu, Mirjalili	February 2022	770	PyTorch, Deep Learning	Definitive PyTorch resource

Conclusion

Commit to working through a selection of these books and you‘ll be well on your way to becoming an intermediate data scientist. The key is to find a few that resonate with you and systematically study them from cover to cover. Don‘t just passively read – work through the examples, complete the exercises, and build projects to apply your newfound knowledge.

With a strong foundation from these books, you‘ll be ready to continue your data science journey into more advanced and specialized topics. Remember that becoming a great data scientist is a marathon, not a sprint. These books provide an excellent starting point, but ultimately your growth will come from consistent practice and application.

So pick a book, crack it open, and start your transformation into a data science star! Just imagine where you could be a year from now if you commit to studying a few pages every day. It won‘t be easy, but it will certainly be worth it.

Happy learning!

[^1^]: Analytics Insight. (2020). Data Science Platform Market to Reach $25.94 Billion by 2027. https://www.analyticsinsight.net/data-science-platform-market-to-reach-25-94-billion-by-2027/

[^2^]: Wes McKinney. (2017). Python for Data Analysis. https://www.oreilly.com/library/view/python-for-data/9781491957653/