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The danger of bias hasn't been given enough consideration in Data Science. Bias is anything that would cause us to skew our conclusions and not treat results and evidence objectively. Bias is sometimes unavoidable, sometimes accidental and unfortunately sometimes deliberate. While bias is well recognised as a danger in mainstream science, I think Data Science could benefit from improving in this area. In this post I categorise the types of bias encountered in typical Data Science work. I have gathered these from recent blog posts , ,  and a discussion in my PhD thesis . I also show how to reduce bias using some of the principles you can learn about in Guerrilla Analytics: A Practical Approach to Working with Data.
Several topical questions were recently asked on Data Science Central. This post addresses the question “What best practices do you recommend, when starting and working on enterprise analytics projects?” I have worked as a Data Scientist for 8 years now. This was after completing a PhD on “Design of Experiments for Tuning Optimisation Algorithms”. So I have a formal background in rigorous experiment design for Data Science and have also managed some pretty complex and fast paced projects in sectors including Financial Services, IT, Insurance, Government and Audit.
One of the biggest challenges with writing a significant piece like a book chapter or entire book is to estimate how long it will take and plan accordingly. My best reference was my PhD which was still significantly shorter than the book’s target 90,000 words. This blog post is about the book writing process as I experienced it. I hope it helps other authors setting out on such an endeavour. Since 'Guerrilla Analytics: A Practical Approach to Working with Data' is about operational aspects of agile data science, I recorded some data on the book writing process itself. Specifically, every time I finished a writing session, I recorded the number of words I’d written on that date.