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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.
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Data Science projects aren’t a nice clean cycle of well defined stages. More often, they are a slog towards delivery with repeated setbacks. Most steps are highly iterative between your Data Science team and IT or your Data Science team and the business. These setbacks are due to disruptions. Recognising this and identifying the cause of these disruptions is the first step in mitigating their impact on your delivery with Guerrilla Analytics.