Jupyter Notebook: Interactive Computing and Computational Narratives

Related lectures (32)

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Explores data manipulation, exploration, and visualization in data science projects using Python.

Explores the analysis of structures and mechanisms through a sample problem of opening a box with a string-held lid.

Introduces functions in Python, covering predefined and user-defined functions, formal and effective parameters, and the importance of docstrings.

Explores file handling and exceptions in Python programming, covering reading, writing, and error handling strategies.

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Covers the basics of Python programming, focusing on flow control structures, interactive mode, script mode, and variables.

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Covers the definition of an inductive data type in Coq and how to build proofs interactively using tactics.

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