Overcoming Common Barriers to Asking Data Science Questions

In a previous post, Getting Better Data Analytics Questions, I stress the importance of asking compelling questions when serving as a member on a data science team. After all, questions are the impetus for exploration and discovery. In that post...

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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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Three Places to Look for Data Analytics Questions

In a previous post, Getting Better Data Analytics Questions, I recommend a couple ways to get the get the ball rolling when it comes to getting people in your organization to start asking compelling questions. However, getting people to ask...

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Getting Better Data Analytics Questions

The success of any data science initiative hinges on the team's ability to ask interesting questions that are relevant to the organization's success and its ability and willingness to challenge assumptions and beliefs. After all, without...

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Conducting Data Analytics in Sprints

In my previous post, Building a Data Science Life Cycle (DSLC), I encourage you to adopt a structure for your data team's activities that is conducive to the type of work it does — exploration. I refer to this structure as the Data Science...

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Building out a Data Science Life Cycle (DSLC)

A project lifecycle can be a useful tool for structuring the process that a team follows. (A lifecycle is a repeating series of steps taken to develop a product, solve a problem, or engage in continuous improvement.) It functions as a...

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Recognizing the Challenges of a Data Science “Project”

In a previous post, Conducting a Data Science "Project", I pointed out some of the key differences that separate data science from traditional project management. While traditional project management is focused more on goals, planning, and...

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Comparing Software Projects to Data Science Projects

In my previous post, Conducting a Data Science "Project", I pointed out the differences between project management and data science. These differences are summarized in the following table:

Project Management

Data Science

Planning

...

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Conducting a Data Science “Project”

The heartbeat of most organizations can be measured in projects. Various teams across the organization set goals and objectives, develop plans for meeting those goals and objectives, and then implement those plans in the hopes of executing their...

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Defining Areas of Responsibility for Your Data Science Team

Scottish novelist and folklorist Andrew Lang once wrote, “I shall try not to use statistics as a drunken man uses lamp-posts, for support rather than for illumination.” Unfortunately, many organizations that consider themselves...

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