Teaching with Notebooks: Getting Student Feedback

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Related lectures (30)

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Python Lists: Manipulation and Comprehension

Covers Python list manipulation and comprehension, emphasizing memory representation and mutability.

Advanced Pandas Functions

Covers advanced functions of Pandas, focusing on filtering, labeling, and manipulating dataframes.

Advanced Pandas Functions

Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

Digital Urban History: Lausanne Time Machine

Delves into the Lausanne Time Machine project, focusing on digital urban history and interactive historical map creation.

Jupyter Notebooks for Numerical Analysis

Covers the transition to Python and Jupyter notebooks for Numerical Analysis at EPFL, including interactive exercises and graded tests.

Interatomic Potentials: LJ and EAM

Explores LJ and EAM interatomic potentials, fitting to material properties and challenges in empirical potential fitting.

Boolean Types and Control Structures

Explores boolean types, logical operators, and control structures in Python, emphasizing the evaluation of expressions and the use of relational operators.

Numerical Analysis: Introduction to Computational Methods

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Defects in Crystals: Point Defects and Dislocations

Explores point defects and dislocations in crystals, highlighting their impact on material properties and the importance of energy relaxation.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.