Teaching with Notebooks: Getting Student Feedback

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Related lectures (30)

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Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

String Operations: Basics and Methods

Covers the basics and methods of string operations in Python, including slicing, indexing, and formatting.

Introduction to Continuum Mechanics

Covers scaling, mathematical modeling, tensor notation, and Python exercises in Jupyter notebooks.

Jupyter Notebook: Interactive Computing and Computational Narratives

Introduces Jupyter Notebook for composing interactive programs with live code and narrative text, covering installation, code cells, Markdown text, widgets, and kernel management.

Exception Handling in Python

Covers exception handling in Python, including try, raise, except, else, and finally blocks, with examples.

Dictionary Operations

Covers operations and methods related to dictionaries in Python, including creating, updating, and handling key errors.

Introduction to Python Programming

Introduces Python programming basics using Anaconda and JupyterLab, with practical exercises on syntax and libraries.

Introduction to Python: Basics and Anaconda Setup

Covers the basics of Python programming and setting up Anaconda and Jupyter Notebook.

Rhythmic Generation Techniques

Covers rhythm generation techniques, including Markov models and hierarchical rhythm generation, with a focus on Nancarrow's Study 14.