This chapter explores how traders’ performance may be influenced by the rationality levels of their peers in the market. Using a Behavioural Data Science approach, the study integrates experimental methods, machine learning and large-scale digital trace analysis to examine this relationship. Specifically, we analysed data from a cryptoasset exchange over a five-week period in late 2017 and early 2018, covering over 700,000 transactions across 17 trading pairs. We complemented this behavioural trace data with an online guessing game involving 2,622 active traders, of whom 273 participated. By combining survey results and trading histories, we applied clustering algorithms to identify seven distinct trader profiles, including ‘jokers’, ‘focal point traders’ and those operating at different levels of strategic reasoning (first, second and third order), as well as ‘professional’ and ‘Nash equilibrium’ traders. The findings suggest that traders engaging in higher-order reasoning generally achieve better financial outcomes, yet even experienced professionals are not immune to behavioural biases. The chapter highlights how Behavioural Data Science methods – linking experimental insight with real-world data and computational tools – can illuminate the cognitive patterns underlying economic decision-making in digital markets.

The ABCs – attention, behaviour and consequences – provide a framework for understanding approaches to behavioural influence. They help identify what initiated a behaviour, why a certain action was taken, why some actions are not completed and how to motivate the desired actions through consequences. The ABCs are a lens to organise existing frameworks and identify what to look for in new approaches. There are various strategies and techniques that fall under the lens of ABC, such as the EAST framework, Thaler and Sunstein’s nudge approach and boosting. These approaches focus on making behaviour easy, capturing attention and reducing effortful processing. Although human behaviour is complex, triggering behaviour, helping it to happen and making the ending worthwhile are a good rule of thumb. Data science can help identify influential factors, but they are more likely to be successful when informed by strong behavioural intuitions. It is important to test these strategies and techniques through experiments and A/B testing. While none of these strategies will work in every situation, most are supported by the weight of evidence, including meta-analyses that combine research from numerous studies.

The Outlook argues that whatever the participant or mediatory statuses of machines may be in future research with large and complex datasets, understanding their uses and impacts at the granularity of human interaction and social accountability will be essential for attempts to integrate human and machine learning from data. It highlights the uses of the two notions of accountability addressed in this book, identifies a spectrum of natural scientists’ social inquiries that hints at their normative orientations, and argues for the use of ethnography as a reminder of human agency and responsibility.

This chapter introduces Python as a powerful yet beginner-friendly programming language essential for data science. It covers getting access to Python through direct installation or integrated development environments like Anaconda and Spyder. The chapter teaches fundamental programming concepts including basic operations, data types, and key data structures (lists, tuples, dictionaries, sets, and DataFrames). Students learn to write control structures using if-else statements and while/for loops, create reusable functions, and make programs interactive through user input. The chapter also explains how to install and use Python packages, which extend the language’s capabilities for specialized tasks. Throughout, practical examples demonstrate concepts like leap year calculations, temperature categorization, and sales data analysis. The chapter emphasizes Python’s accessibility, extensive package ecosystem, and suitability for data science applications, positioning it as an ideal tool for solving computational and data analysis problems.

The traditional case register involved assembling records of people with a given condition in order to support cohort studies to describe and investigate the course of their condition and other outcomes. This old design has been resurrected and revolutionised following the widespread implementation of fully electronic healthcare records over the past few decades, providing ‘big data’ resources that are both large and very detailed. These, in turn, are being further enhanced through linkages with complementary administrative data (both health and non-health) and through natural language processing generating structured meta-data from source text fields. This chapter provides an overview of this rapidly developing research infrastructure, considering and advising on some of the challenges faced by researchers planning studies using clinical data and by those considering future resource development.

This chapter discusses the broader role and impact of analytics science in improving various aspects of society. It introduces what the book is about, and what the reader should expect to learn from reading this book. It also discusses the analytics revolution in the private and public sector, and introduces a key element of the book — insight-driven problem solving — by highlighting its vital role in addressing various societal problems.

This chapter is devoted to data analysis and its critical role in analytics science. The reader is introduced to the science of inference from observations and experiments and learns about the main ideas in data analysis that have been influential in addressing societal problems. Real-world examples are used throughout to convey the main ideas and illustrate why data analyses performed without sufficient care can yield wrong insights. Successful examples of insight-driven problem solving approaches in data analysis are contrasted with those that can yield wrong insights, and the reader is taken on an engaging yet educational journey that depicts how and why successful insight-driven problem solving approaches using data can have significant public impact.

This chapter introduces the reader to the big picture of what analytics science is. What is analytics science? What types does it have, and what is its scope? How can analytics science be used to improve various tasks that society needs to carry out? Is analytics science all about using data? Or can it work without data? What is the role of data versus models? How can one develop and rely on a model to answer essential questions when the model can be wrong due to its assumptions? What is ambiguity in analytics science? Is that different from risk? And how do analytics scientists address ambiguity? What is the role of simulation in analytics science? These are some of the questions that the chapter addresses. Finally, the chapter discusses the notion of "centaurs" and how a successful use of analytics science often requires combining human intuition with the power of strong analytical models.

Chapter 13 presents the second application of MATLAB to behavioral sciences: data analysis. Students review previously-learned data structures often encountered in practice before applying their programming knowledge from Chapters 1 to 11 to manage each. Starting with tabular data, tables from Chapter 8 are reviewed, with students learning common data science tasks for managing one or more tabular data sets, before applying their knowledge to real experimental data. Next, hierarchical data are reviewed, connecting students’ knowledge of structure arrays from Chapter 8 to a popular internet-based data format (JSON), with students applying their newfound knowledge to analyze data on the behavior of European monarchs.