Technical books we love related to data, computing and machine learning.

## Data Science

**Agile Data Science 2.0** - Russel Jurney

*This book is for:* data scientists looking for a complete philosophy of data science.

*Our highlight:* arguments for data scientists to be generalists, able to sprint throughout the data pyramid.

**Data Science from Scratch: First Principles with Python (Second Edition)** - Joel Grus

*This book is for:* those who understand their tools by seeing them work from the inside.

*Our highlight:* Chapter 17 on decision trees - building up the concepts from a single tree to a random forest.

**Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (Second Edition)** - Wes McKinney

*This book is for:* those looking for an introduction to the SciPy stack.

*Our highlight:* the mixture of a top down and bottom up approach to learning - the book starts off with a top down look at using Python to analyse three datasets.

## Machine Learning

**Deep Learning** - Ian Goodfellow and Yoshua Bengio and Aaron Courville

*This book is for:* anyone interested in the theory of neural networks and deep learning.

*Our highlight:* Chapter 3 for an excellent introduction to the probability background needed for deep learning.

**Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (Second Edition)** - Aurélien Géron

*This book is for:* those looking for a practical guide to working with modern machine learning.

*Our highlight:* Chapter 3 on classification, including a useful explanation of precision and recall.

**Pattern Recognition and Machine Learning** - Christopher M. Bishop

*This book is for:* anyone looking for a thorough, statistical look at classical machine learning.

*Our highlight:* section 5.6 on Mixed Density Networks - one of our favourite neural network architectures. Great for multi-modal regression problems.

**Reinforcement Learning: An Introduction (Second Edition)** - Richard S. Sutton and Andrew G. Barto

*This book is for:* any interested in reinforcement learning - it’s called the Bible of reinforcement learning for a reason.

*Our highlight:* Chapter 2 on multi-armed bandits - a useful tool for any data scientist running A/B tests.

## Programming

**Clean Architecture: A Craftsman’s Guide to Software Structure and Design (First Edition)** - Robert C. Martin

*This book is for:* anyone looking for a complete philosophy on how to design software.

*Our highlight:* an emphasis on well designed software is easy to maintain.

**Designing Data-Intensive Applications** - Martin Kleppmann

*This book is for:* data professionals who want to go under the hood and understand how large data tools work.

*Our highlight:* Chapter 10 on batch processing with UNIX tools.

**The Pragmatic Programmer: From Journeyman to Master (20th Anniversary Edition)** - Andrew Hunt & David Thomas

*This book is for:* those looking for a book you can dip in and out of - learning something new each time you pick it up.

*Our highlight:* Topic 18 on power editing - full of useful tips on how to use a text editor efficiently.

## Python

**Effective Python: 90 Specific Ways to Write Better Python (Second Edition)** - Brett Slatkin

*This book is for:* those looking to write clear Python code. We have loved the additions made in the Second Edition. Contains advice for complete beginners and experienced Python developers alike.

*Our highlight:* Item 10, covering assignment expressions using the walrus operator (`:=`

) introduced in Python 3.8.

**Fluent Python** - Luciano Ramalho

*This book is for:* those wanting to dive deeper into how Python works behind the hood.

*Our highlight:* Chapter 4, which demystifies encoding of text and binary strings in Python 3.

**Python Testing with pytest** - Brian Okken

*This book is for:* anyone wanting to get the most out of their Python test suite.

*Our highlight:* Chapter 3 on using parameterized fixtures - a great way to run multiple cases with one test.

## Statistics

**Bayesian Data Analysis (Third Edition)** - Andrew Gelman et. al

**Practical Statistics for Data Scientists** - Andrew & Peter Bruce

*This book is for:* data professionals wanting to understand the statistical side of working with data.

*Our highlight:* Chapter 3 on statistical significance testing, including a section on multiarmed bandits.

**Think Stats (Second Edition)** - Allen Downey

*This book is for:* data professionals wanting to understand the statistical side of working with data.

*Our highlight:* Chapter 7 on correlation - a must understand concept for every data professional.