Jupyter Notebook: Interactive Computing and Computational Narratives

Related lectures (32)

Page 3 of 4

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

Covers the basics of object-oriented programming in Python, including objects, classes, inheritance, and input/output handling.

Covers data wrangling techniques using Apache Hive for efficient big data management.

Covers common operations on sequences like lists and tuples, including slicing, extending, and replacing elements.

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Covers control operations, algorithmic complexity, function calls, and travel time analysis.

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

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

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.