Lectures related to Scientific Programming for Engineers: Introduction to GIT

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

Explores tools like Git and Docker for collaborative data science projects.

Introduces Git, a version control system essential for software development.

Introduces version control with Git, focusing on repository setup, committing changes, and collaboration.

Covers the setup of a Gitlab agent for Kubernetes, focusing on installation, version control, and troubleshooting.

Introduces the basics of Version Control Systems, focusing on Git operations and branching strategies.

Explores Git's distributed version control features, focusing on conflict resolution and collaboration management.

Introduces Python and Git for analyzing soil humidity data in an orchard.

Explores Git's distributed version control, covering conflict resolution, collaboration management, and merging in software projects.

Covers the basics of Git and GitHub, including creating a GitHub repository and managing remote repositories.

Covers the process of making pâte brisée and crêpes, along with Git version control commands.

Showcases the basics of collaborative work on GitHub through examples of creating repositories, making commits, and managing branches.

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Covers the basics of GIT, including cloning, committing modifications, managing branches, and using remotes.

Introduces the basics of Git, covering setting up repositories, committing changes, and pushing modifications.

Introduces the basics of Git, covering installation, configuration, commits, branches, and repositories.

Covers the fundamentals of version control systems, focusing on Git.

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

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

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