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A Technical Data Professionals Library

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.