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Dashboard Week – Day 2- NCAA Basketball

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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In the second day, we had the opportunity to explore a basketball dataset from Google Big Query. We need to explore the dataset using Power BI. At this point, I’m pretty comfortable with this tool, despite

Getting Started: Connecting to Data

Fortunately for me, connecting to Google Big Query was not much of the challenge compared to web scraping. The process was pretty straight-forward with Power BI. One thing to be mentioned is that connecting to Google Big Query required having a Google Cloud account. It should take only few minutes to set-up, unless you do not own a Gmail account.

Get Down To Business

Basketball is not my cup of tea. I know a thing or two, but definitely not enough to called myself subject matter expert. After a quick scan, I realized that this is a decent-sized dataset, meaning there are many things to look at and analyze. After a quick brainstorming session, I choose Player Table as starting point. In fact, I only used this table for the entire workflow since It has more than enough information to play with. To further reducing the complexity, I applied filter to get desired subset

From Player Table, what make me curious was the number of Asian basketball players in NCAA League. To avoid being overwhelmed, I tend to have a one big question, which eventually lead to another question.

Start Building

Now that I have my anchor point, I slowly build out my dashboard. I tried to answer one question at a time, and only used relevant columns. This approach helped me keep things manageable while letting the data guide the direction of the story.

Final Thought

Focusing on a single table turned out to be a smart move—it kept the scope manageable and allowed me to explore the data more deeply. While I’m not a basketball fan, digging into the player data sparked some interesting observations, especially around representation of East & Southeast Asian-Born players in the NCAA.

Power BI made it easy to filter, slice, and visualize the data as new questions came up. I didn’t try to answer everything—just followed one line of curiosity and let the data take the lead. Overall, it was a solid exercise in building a focused analysis with a tool I’m getting more confident in.

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Dashboard Week – Day 2- NCAA Basketball

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

In the second day, we had the opportunity to explore a basketball dataset from Google Big Query. We need to explore the dataset using Power BI. At this point, I’m pretty comfortable with this tool, despite

Getting Started: Connecting to Data

Fortunately for me, connecting to Google Big Query was not much of the challenge compared to web scraping. The process was pretty straight-forward with Power BI. One thing to be mentioned is that connecting to Google Big Query required having a Google Cloud account. It should take only few minutes to set-up, unless you do not own a Gmail account.

Get Down To Business

Basketball is not my cup of tea. I know a thing or two, but definitely not enough to called myself subject matter expert. After a quick scan, I realized that this is a decent-sized dataset, meaning there are many things to look at and analyze. After a quick brainstorming session, I choose Player Table as starting point. In fact, I only used this table for the entire workflow since It has more than enough information to play with. To further reducing the complexity, I applied filter to get desired subset

From Player Table, what make me curious was the number of Asian basketball players in NCAA League. To avoid being overwhelmed, I tend to have a one big question, which eventually lead to another question.

Start Building

Now that I have my anchor point, I slowly build out my dashboard. I tried to answer one question at a time, and only used relevant columns. This approach helped me keep things manageable while letting the data guide the direction of the story.

Final Thought

Focusing on a single table turned out to be a smart move—it kept the scope manageable and allowed me to explore the data more deeply. While I’m not a basketball fan, digging into the player data sparked some interesting observations, especially around representation of East & Southeast Asian-Born players in the NCAA.

Power BI made it easy to filter, slice, and visualize the data as new questions came up. I didn’t try to answer everything—just followed one line of curiosity and let the data take the lead. Overall, it was a solid exercise in building a focused analysis with a tool I’m getting more confident in.

Share this post