Data Modeling

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Riverbend City ® Activity

Data Modeling Introduction Mentor Talk Conclusion

Introduction Welcome back to your virtual internship at the Riverbend Community Action Center! So far, you have been introduced to your overarching project of using data analytics to help RCAC evaluate the effectiveness of their Ruby Lake Teen Homelessness Task Force, and then talked to staff members to get a sense of the types of questions you should be trying to answer with data.

The next step will be to take a closer look at data modeling.

It’s time for another meeting with your mentor, Brenda.

Mentor Talk

Riverbend City Community Action Center: Mentor’s Office

Check in with your CAC Mentor, Brenda. Come on in! I hope you’re finding your internship interesting and challenging so far.

So, since the last time we talked, you should have a more solid sense of what the overall plan is here, and what kind of questions we need to be answering through data analytics. That’s the first step for any project like

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this, and now it’s time to move forward.

The next thing I’d like to do is to talk to you a little about data modeling. it’s really important; it’s a foundational thing, and we have to make sure everything’s straight before we can proceed. Like, if you get your modeling done correctly, subsequent steps are that much easier and more logical. Get it wrong, and the whole thing is liable to blow up on your face when you’re halfway through the process and you realize you can’t actually answer the questions you’re trying to answer.

First off, what is this data modeling thing? It’s a little abstract. A data model is a conceptual representation of the structure that’s going to guide your database. One way to think of it is that it’s an attempt to properly represent reality through data. Maybe “conceptual blueprint” is a good way to think of it.

Your data model needs to account for the nature of the data you’re gathering, the institutional rules at play in using it, and the organization of the data itself. Think tables, columns, relationships, constraints, and that sort of thing.

There are three types of data models we’re going to think about: relational, statistical, and predictive. These are all different approaches to structuring and handling data, depending on what kind of information you’re collecting and what you want to do with it.

If you have experience using databases, a relational data model might seem like the most intuitive and familiar approach. In this setup, data is – of course- stored in a relational database. Basically, your classic database setup: a series of indexed tables with one-to-one and one-to-many relations set between them, governed by keys. You can manipulate the data and report on it using something like SQL.

Next, statistical data modeling. It lives up to its name, more or less- you’re amassing and storing large amounts of data along some pre- identified variables, with the idea that you can aggregate these datapoints and subject them to statistical analysis. This allows you to identify patterns and correlations, and possibly identify trends that may continue into the future.

And finally, I’d like to talk about predictive data modelling. You can think of it as a modelling approach that’s kind of a means to an end… where the end is being able to predict future outcomes based on the data you’re gathering. In this case, you structure your data model around a series of predictors that you’ve identified; in other words, variables that are likely to have an effect on the outcomes that you’re concerned with. There’s an interplay here between predictive and statistical data

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modelling in that one informs the other; as things move forward, for instance, you might use a statistical data model to evaluate the effectiveness of your predictive model.

I hope this helps! Next, we’ll be talking about putting some of this stuff into practice.

Conclusion You have completed the Riverbend City: Data Modeling activity.

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