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Stephanie Glen
  • Female
  • Jacksonville, FL
  • United States
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Profile Information

Short Bio:
Stephanie Glen is a contributing writer at DataScienceCentral.com. After working for several years teaching mathematics and statistics at the university level, she created and developed the website StatisticsHowTo.com.
Company:
Andale Publishing, LLC
Job Title:
CEO
Seniority:
Professor
Job Function:
Chief AI Officer
Country
USA
Number of employees:
1 to 49
Industry:
Wholesale
Interests:
Contributing
Topics of Interest
Data Science

Stephanie Glen's Blog

E-R Diagram Cardinality and Participation

Posted on July 25, 2021 at 11:00am 0 Comments

Cardinality and participation explained for E-R diagrams.

  • How to show cardinality/participation for several methodologies.
  • Examples of notation and diagrams.

In my previous posts, I discussed some E-R diagram…

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E-R Diagram: 5 Mistakes to Avoid

Posted on July 18, 2021 at 2:53pm 2 Comments

  • Good E-R diagrams capture core components of an enterprise.
  • 5 things not to include in your diagram.
  • Tips to guide you in your E-R diagram design.

In my previous blog post, I introduced you to the…

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Intro to the E-R Diagram

Posted on July 11, 2021 at 2:30pm 0 Comments

  • E-R diagrams capture meaning in your data.
  • This high-level visual tool identifies core components of an enterprise
  • Simple steps to follow to create the diagram.

Entity-Relationship (E-R) Modeling is one approach to visualize what story your data is trying to tell. This goal of this predecessor to…

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Five Graphs to Show Cause and Effect

Posted on June 27, 2021 at 5:30pm 0 Comments



  • Cause-and-effect relationships can be visualized in many ways.
  • Five different types of graphs explained, from simple to probabilistic.
  • Suggestions for when to use each type.

If your data shows…

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At 3:52am on December 11, 2019, Jason Posavad said…

Your article is so relevant Stephanie, as I am 48 and contemplating an online masters in Data Science.  My answer has been to do as much as I can through MOOCs, self-driven projects, and other less-expensive learning.  If it gets to the point that I have done all of that and proven to myself that I can do Data Science, and that I need a degree to get land a job, I will then do it.   Reality is you do need to be more careful and more risk-averse as you age, as the time to recover from mistakes is just not there anymore.  

 
 
 

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