About this episode

Kelly Schuster-Paredes interviews Mahmoud Harding about data science education and effective teaching methods for Python and R.

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…

People in this episode

Host: Kelly Schuster-Paredes

Guest: Mahmoud Harding

Topics covered

• data science education
• teaching Python
• project-based learning
• R vs Python
• student engagement
• data interpretation

Keywords

• data science
• Python
• R
• project-based learning
• student engagement
• Jupyter
• statistics

Mentioned in this episode

Organizations: Data Science for Everyone

Products: Python, NumPy, pandas, Jupyter Everywhere

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