A Christmas wish-list of books from the FT Teams
Twelve reads from the FT Teams’ list
Each year Tom Betts, Chief Data Officer at the FT, asks his team to name a book of their choice ahead of the festive break.
This year, we wanted to share twelve reads from the FT Teams’ list; spanning business, data and the wider world.
1. Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado Perez
Winner of this year’s FT & McKinsey Business Book of the Year, Invisible Women exposes a world made for men, by men: from medical tests to office temperature control. The book successfully argues that the lack of big data on women means an entire gender is rendered invisible.
2. Storytelling With Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic
They say good storytelling is the most impactful way to motivate, persuade and truly engage your listeners. Knaflic’s book helps anyone communicating with data to connect more effectively with an audience, taking learnings from how the mind perceives graphical images to ensure the audience gets the appropriate insights from the data presented.
3. Time to Think: Listening to Ignite the Human Mind by Nancy Kline
Kline’s booktells the story of the 'Thinking Environment’, a model of human interaction that dramatically improves the way people think, and thus the way they work and live. Listening - the quality of people's attention for each other - is the central pillar of this method.
4. She’s Back: Your guide to returning to work by Lisa Unwin and Deb Khan
Well regarded as an essential read for women looking to make a great return to work after a break, She’s Back prompts women to really focus on the positives that come from some time away and how to craft the optimal narrative, rather than falling into a cycle of apologising for it.
Unwin and Khan have pulled together years of research from multiple industries, backed by thousands of rich, relatable stories from women. This book will arm anyone with a fresh, pragmatic and truly useful handbook for today’s rapidly evolving job market.
5. The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns and Aaron Roth
In a modern era where algorithms are producing unintentionally biased results - from consumer credit fiascos to unfairly harming job applicant chances, this book provides a framework that combines human judgment with machine learning and system design to help us navigate the biases at play.
6. Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll and Benjamin Yoskovitz
Lean Analytics acts as an all too vital guide to work out the optimal business model to adopt at the particular stage of your business’ growth. It’s based around the principle of finding the ‘One Metric That Matters’ to you right now, when to stick with an idea, and when to twist.
7. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures by Claus Wilke
A brilliant manual for any data scientist - new or experienced - who wants to convey the nature of data succinctly, accurately, and in a visually engaging way.Wilke’s book features tonnes of examples of the bad, the ugly and the best ways to visualise information.
8. The Right It: Why so many ideas fail and how to make sure yours succeed by Alberto Savoia
The Stanford professor takes readers through an innovative framework for beating the beast that is market failure. His concept of 'pretotyping' sets out a way of getting meaningful feedback about an idea rapidly, and then quickly validating there's a market for the idea in days or weeks, rather than months or years.
9. Emerging Perspectives in Big Data Warehousing by David Taniar
An essential research publication that explores current innovative activities on the integration between data warehousing and data mining.This book emphasises how best to apply these concepts to significant real-world problems, featuring a wide range of topics such as index structures, ontology, and user behaviour.
10. Deep Learning (Adaptive Computation and Machine Learning Series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach
Hailed as the ‘bible’ of deep learning - this is a fascinating introduction to a broad range of topics in deep learning. It covers the mathematical and conceptual background through to deep learning techniques used in industry and research. Deep Learning has also received a glowing review from Tesla CEO Elon Musk as “the only comprehensive book on the subject”.
11. SQL: The Ultimate and Simplified Guide to Mastery SQL Programming by Johnny Page
As the most popular database language in the world, SQL (Structured Query Language) is well worth knowing for those looking to extract insights from data: whether you’re a marketeer, developer or data scientist. The book teaches how almost all answers are in data, you just need to ask the right question (and write the right SQL query).
12. The Art of Statistics: Learning from Data by David Spiegelhalter
Spiegelhalter guides the reader through the essential principles we need to grasp in order to extract knowledge from data. The book is sprinkled with examples of how to read and present data in visual form, but where it really stands out, is its ability to draw on real world problems to introduce conceptual issues.
We’re shown how statistics can help us determine whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. A must read for understanding how statistics work in the world around us.
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