Missing Data Leads to the Wrong Conclusions

In my previous post, Challenging the Evidence in Data Science, I encourage data science teams to be skeptical of any claims or evidence that supports those claims, and I provide several techniques for challenging claims and evidence.

However,...

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Challenging the Evidence in Data Science

Data drives the data science team's exploration and discovery, so the team must be on the constant lookout for bad data, which can lead the team astray or result in erroneous conclusions. In this post, I present several ways to challenge the data...

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Working Together on a Data Science Team

As I explained in a previous post, Building a Top Notch Data Science Team, a data science team should consist of three to five members, including the following:

  • Research lead: Knows the business, identifies assumptions, and drives questions....
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Building a Top Notch Data Science Team

Building a data science team is not as simple as hiring a database administrator and a few data analysts. You want to democratize your data — you want the organization’s data and the tools for analyzing it in the hands of everyone...

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Steering Clear of Common Data Science Pitfalls

Many people have the misconception that science is equivalent to truth. In fact, people often cite science as the authority on a specific issue. They seem to believe that anyone who challenges scientific claims is challenging the truth and in...

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Five Approaches to Statistical Analysis

Data science teams capture, store, and analyze data to extract valuable information and insight. In recent posts, I focused on capturing and storing three types of data — structured, semi-structured, and unstructured — and I encouraged...

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