Teaching Python

165 Episodes

Reverse

Episode 164: The Science of Remembering

2026-10-1657:57

How much of what we learn do we actually remember? And what if one of the most effective ways to learn is to regularly challenge ourselves to recall what we already know? In episode 164 of Teaching Python, Kelly Schuster-Paredes and Julian Sequeira welcome Python educator and Python Morsels creator Trey Hunner to explore the testing effect, retrieval practice, spaced repetition, and interleaving. Trey shares how learning the countries of the world through his geography app, Whereabouts.Earth, helped shape Daily Recall, a spaced repetition learning system for Python programmers. The conversation goes beyond remembering the right answer to ask whether we understand what we know. Are we recalling a concept or recognizing the shape of a familiar question? Can we apply what we have practiced when the context changes? From Duolingo to Python exercises, the hosts explore why recall needs to work alongside mental models and practical experience, and why learning that feels harder can be more useful over time. They also examine what this means when AI can generate code for us. Trey, Kelly, and Julian discuss the difference between learning something deeply and getting a task done, how much understanding we need to evaluate AI-generated work, and why foundational knowledge still matters for making informed decisions. Show Links Python Morsels Jev for Beginners Pilots still practice Linus writes code with AISpecial Guest: Trey Hunner.

Episode 163: How Do You Find Time to Keep Learning?

2026-10-0252:40

How do you keep learning when there never seems to be enough time? The answer may be less about finding more time and more about building learning into the way you already work, solve problems, use AI, and connect with other people. In Episode 163 of the Teaching Python Podcast, Sean Tibor, Kelly Schuster-Paredes, and Julian Sequeira discuss how they keep learning in a technology landscape shaped by rapid changes in AI, software development, and computing. They compare practical approaches to continuous learning, including building projects, using commutes and downtime, scheduling dedicated learning time, asking ChatGPT and Claude questions as they come up, watching webinars, reading, experimenting with new tools, and learning from professional networks. The conversation also explores what happens when you need to learn something you are not naturally interested in. They discuss adult learning, desirable difficulty, just-in-time learning, recursive learning, and why struggling with unfamiliar ideas can still be valuable even when AI can explain concepts instantly. Along the way, they talk about vibe coding, AI as a learning partner, professional development, technical communities, balancing creation and consumption, and setting boundaries so that continuous learning does not become constant burnout. If you are trying to keep up with AI, learn new technology, or continue developing technical skills without turning every spare moment into work, this episode offers a practical look at how learning can become part of everyday life.

Episode 162: What Does “Teaching Python” Mean in 2026?

2026-09-1929:53

What does teaching Python mean in 2026, when AI can generate code and programming sits inside a much larger computing landscape? Teaching Python started as a podcast about teaching programming in the classroom, but both the hosts and the world around Python have changed. In this episode, Kelly and Julian reflect on how Python education now connects with AI, data, cybersecurity, cloud computing, software engineering, systems thinking, and automation. They discuss how their own roles have evolved from teacher, coder, and technologist into work that crosses disciplines, and why understanding technology now requires more than simply learning how to write code. The conversation explores why learning Python still matters in the age of AI-generated code, why reading, debugging, evaluating, testing, and modifying code may be more important than ever, and how teachers and learners can build the judgment needed to work with increasingly capable AI tools. Python is still here. But teaching Python in 2026 is increasingly about how people learn, build, reason, and make decisions with technology.

Episode 161: Teaching Hard Things Simply

2026-08-0701:03:59

In this episode of Teaching Python, Kelly and Julian welcome IBM Distinguished Engineer Jeff Crume to talk about teaching hard things simply. The conversation begins with Jeff’s short, visually driven videos and the studio setup behind them, including the lightboard format, the editing process, and the amount of planning needed to turn a 15-minute explanation into something clear and usable. They then turn to the challenge of explaining complex ideas in a way that fits the audience. Jeff describes how he chooses topics from his work with clients, why he thinks teaching deepens his own understanding, and how he adapts for YouTube, classrooms, and conference talks. He emphasizes brevity, structure, and using visuals so viewers are not faced with a talking head and a blank background. A major part of the discussion focuses on AI, the humanities, and education. Jeff explains why he believes the humanities are essential for understanding meaning, purpose, truth, and context, and why those questions matter when using AI. He argues that AI should be treated as a tool to augment learning rather than something to exclude from classrooms. The conversation also covers cybersecurity and practical AI risks. Jeff discusses passwords versus passkeys, phishing, public chatbots, data privacy, cloud services, and the security concerns around agents and connected tools. He argues for private instances, stronger security practices, and doing security earlier in the process. Near the end, Jeff highlights communication, curiosity, and critical thinking as key skills for students. He also points listeners to IBM SkillsBuild and Coursera for training, and closes by encouraging lifelong learning in a fast-changing field.Special Guest: Jeff Crume.

Episode 160: Data Science, Math and Python, Oh My!

2026-07-1601:00:08

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 the instructional design director at Data Science for Everyone, where the goal is to make data science available to more students and to connect it to meaningful, real-world contexts. A major part of the conversation focuses on how students learn best through curiosity and project-based work. Mahmoud describes the ADAPT model, including its emphasis on project-based learning and common learning elements, and he argues that students should begin working with their own data early in a course. Kelly and Mahmoud discuss how choosing their own datasets helps students become more engaged, notice mistakes, and ask better questions. The discussion also compares R and Python as tools for data science. Mahmoud explains that R was designed by statisticians for statistical analysis, while Python became popular as a general-purpose language that later grew into a strong data science ecosystem through libraries like NumPy and pandas. He also describes Jupyter Everywhere, a browser-based notebook environment designed to reduce barriers for schools and allow students to use R or Python without complicated setup. Later, the conversation turns to judgment, nuance, and the role of data in learning. Mahmoud argues that students need domain knowledge and human judgment to interpret data responsibly, and that data projects can help them develop those skills. Kelly extends this idea to other subjects, suggesting that books, history, and other classroom materials can also be treated as data for analysis and discussion. The episode closes with Mahmoud sharing ways to connect with him through Data Science for Everyone and with mention of an upcoming Data Science Education K–12 event in Atlanta in February.Special Guest: Mahmoud Harding.

#box-pro-ellipsis-179333486692799{-webkit-line-clamp:2;}Teaching Python