Introduction to Jupyter Notebook

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Scientific Programming for Engineers: Introduction to GIT

Covers the basics of scientific programming for engineers, emphasizing the importance of GIT for collaborative work and providing insights into challenges in scientific software development.

Introduction to NumPy and Matplotlib for Scientific Computing

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

Version Control with Git

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

Introduction to NumPy: Basics of Scientific Computing

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Data Wrangling with Hive: Managing Big Data Efficiently

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

Gitlab Agent for Kubernetes (`agentk`)

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

Introduction to Data Science

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

Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods

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

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.