Jul 16, 2026 · 1 hr · 15 segments
In this episode, Kelly Schuster-Paredes speaks with Mahmoud Harding about his work in data science education and the way he thinks about teaching Python, R, and statistics. Mahmoud explains that he is…
Mahmoud HardingGuest
Kelly Schuster-ParedesHostThere's two stories I wanna connect to that because I think this, this... it's so important, and for my answer to the, my question that I asked you, will generative AI make computer science obsolete? Like for me, I still think absolutely no.
So Python, show someone Matplotlib, you know, you get-- You just copy it, have them change the data.
It's like they don't even really need to read Matplotlib because it reads itself, and they get a chart.
And because it's all they have to do is change the data within the X list or the Y list, you know, they can easily make a, you know, a bar graph or a line chart, depending on what they do.
Even though AI can ge- generate a graph that's so much prettier, the fact that they wrote like six lines of code and it just popped a graph out, I think that, that to me says a lot about the beauty of visualizations and the fact that once they then get into, you know, AI and they do some Pyplots, or they use some other library in there, and it makes a really cool graph, then they're like, "This is cool."
I work with a girl who just graduated with a data science degree, and I took, you know...
I'd struggled through a Georgia Tech boot camp, six months of data science, three nights a week until 10 o'clock at night, and I loved it, and I hated it.
It was machine learning, data science, Python, you name it, smushed into six months.
The people that succeeded and got jobs, uh, they deserved it because they were rock stars.
And I work with this girl who just graduated, and she, when we started working on this project, we have this special prog- project at school, um, she was c- she would come to me, she's like: "Oh, I did this, and I, I saw this." And I was like: "Oh my God, I didn't even see that, and you looked at like 5,000 points of data, and that's the pattern you got." And I think that That right there is one of the reasons why if you can get someone to learn data science, the brilliance just shines through.
So when I'm teaching students how to visualize in Python, when I first started teaching, I wanted them to make the same graphs that I was making with my data so that they would learn how to do it.
I was like, "Well, let them choose their own data and make their own graphs." And they learn so much more when they see that they've chosen the wrong kind of graph to visualize a certain type of data, or when they look at their graph and they're wondering, "How come my line chart doesn't look like a line chart? What did I do that was wrong?" And it motivates them in a different way because now they're invested in solving their own problem.
There's two stories I wanna connect to that because I think this, this... it's so important, and for my answer to the, my question that I asked you, will generative AI make computer science obsolete? Like for me, I still think absolutely no.
So Python, show someone Matplotlib, you know, you get-- You just copy it, have them change the data.
It's like they don't even really need to read Matplotlib because it reads itself, and they get a chart.
And because it's all they have to do is change the data within the X list or the Y list, you know, they can easily make a, you know, a bar graph or a line chart, depending on what they do.
Even though AI can ge- generate a graph that's so much prettier, the fact that they wrote like six lines of code and it just popped a graph out, I think that, that to me says a lot about the beauty of visualizations and the fact that once they then get into, you know, AI and they do some Pyplots, or they use some other library in there, and it makes a really cool graph, then they're like, "This is cool."
I work with a girl who just graduated with a data science degree, and I took, you know...
I'd struggled through a Georgia Tech boot camp, six months of data science, three nights a week until 10 o'clock at night, and I loved it, and I hated it.
It was machine learning, data science, Python, you name it, smushed into six months.
The people that succeeded and got jobs, uh, they deserved it because they were rock stars.
And I work with this girl who just graduated, and she, when we started working on this project, we have this special prog- project at school, um, she was c- she would come to me, she's like: "Oh, I did this, and I, I saw this." And I was like: "Oh my God, I didn't even see that, and you looked at like 5,000 points of data, and that's the pattern you got." And I think that That right there is one of the reasons why if you can get someone to learn data science, the brilliance just shines through.
So when I'm teaching students how to visualize in Python, when I first started teaching, I wanted them to make the same graphs that I was making with my data so that they would learn how to do it.
I was like, "Well, let them choose their own data and make their own graphs." And they learn so much more when they see that they've chosen the wrong kind of graph to visualize a certain type of data, or when they look at their graph and they're wondering, "How come my line chart doesn't look like a line chart? What did I do that was wrong?" And it motivates them in a different way because now they're invested in solving their own problem.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
