Critical Thinking

Some time ago I started writing a post on data preparation which I never completed and eventually forgot. A recent LinkedIn post by Kevin Gray stimulated a rich conversation around: “Can Data Cleaning be automated?”. It reminded and enticed me to complete the post.

Data Cleaning and Data Preparation When practitioners talk about data cleaning, they usually refer to a collection of tasks needed to make the data amenable for analysis.

Everyone seems to like top-10 lists and many organizations are interested in Big Data, so it seems timely to write my own top 10 list on Big Data. A premise is warranted. Those who know me, know how much I ditest the term “Big Data”. Yet, for good or worse, Big Data is here to stay and so it’s important that we try clarify what it is and it isn’t.

First published in SSC Liason, Amstat News There is no question in my mind that statisticians are crossing a sea of changes. As a profession, we have made high-quality contributions to many fields over the past decades, with our engagement being perfectly epitomized in the recent book Statistics in Action: A Canadian Outlook. However, one cannot help but notice the recent trends (and hype) in the closely aligned—and somewhat vaguely defined—fields of analytics, Big Data, data science, and machine learning and wonder if our current model will continue to do well.