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The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
The chapter outlines social media and qualitative research. It describes social media for data collection and different qualitative research approaches to data collection. The chapter describes social media as a phenomenon for research and outlines different levels of social media utilization: individual, work-practice and supra-organizational levels. Vignettes for the different levels are provided and the need for qualitative research concluded.
In collaboration with the HR team of a large IT service provider, this chapter relates to a study of fifty individuals who have been identified as high performers by their employer and the search for indicators and patterns of sustainable high performance.
The research design consisted of initial interviews at a virtual day, attendance of 2.5-day off-site coaching workshops and up to 60-minute follow-up interviews. During the workshop days, 24-hour heart rate variability (HRV) measurements were collected – a well-established biomarker of well-being, strain and recovery. As HRV data are difficult to analyze without contextual information, interviews, focus-group sessions, participatory observation and debriefing interviews were combined in order to contextualize the quantitative measurements and involve the participants in the interpretation and sense-making of the findings.
The methodological goal of this chapter is to demonstrate how orchestrating, improvising and performing a mixed-method study has been essential to validate, augment and complement quantitative data. The study results depend on the ability of the researchers to skilfully and empathetically engage with the interviewees and to engage them as participants in the interpretation of their data and thus as co-producers of meaning.
Recognizing the pervasive influence of modern digital technologies, this chapter argues for the supremacy of strategy work in terms of giving shape and effect to the associated agenda for strategic, organizational and technological change. The chapter focuses on the theory and practice of action research as a Mode 2 approach to knowledge production as managers co-inquire into the practice of strategizing. The discussion speaks directly to the practice of action research in government organizations, of enhancing strategy work and its related outcomes, and the broader outcomes of co-inquiry. The chapter affirms the central role of action research in knowledge production and emphasizes how the practice of action research is itself being transformed by enabling digital technologies during the current COVID-19 pandemic. The contention throughout is that good practice informs research and good research informs practice.
This chapter advocates further advancing qualitative research methods by creating tools to investigate digital traces of digital phenomena. It specifically focuses on large-scale textual datasets and shows how interactive visualization can be used to augment qualitative researchers’ capabilities to theorize from trace data. The approach is grounded on prior work in sense-making, visual analytics and interactive visualization, and shows how tasks enabled by visualization systems can be synergistically integrated with the qualitative research process. Finally, these principles are applied with several open-source text mining and interactive visualization systems. The chapter aims to stimulate further interest and provide specific guidelines for developing and expanding the repertoire of open-source systems for qualitative research.