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Stephanie Glen's Page
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 0 Likes
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…
ContinueE-R Diagram: 5 Mistakes to Avoid
Posted on July 18, 2021 at 2:53pm 2 Comments 0 Likes
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…
ContinueIntro to the E-R Diagram
Posted on July 11, 2021 at 2:30pm 0 Comments 0 Likes
- 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…
ContinueFive Graphs to Show Cause and Effect
Posted on June 27, 2021 at 5:30pm 0 Comments 1 Like
- 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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Posted 20 July 2021
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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.