Data Science Visualization with Pandas

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Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

Covers the essentials of Data Science using Python, Numpy, Pandas, and Scikit-learn, including DNA sequence analysis and classification.

Matlab: 3D Surface Plotting

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Python Lists: Manipulation and Comprehension

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

3D Stone Scanning Session

Introduces a 'professional' 3D measurement system for stone analysis and feature extraction using stereo photogrammetry and structured light technologies.

Big Data Best Practices and Guidelines

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

Collaborative Data Science

Covers collaborative data science tools, big data concepts, Spark, and data stream processing, with tips for the final project.

Matlab Programming: Script and Function

Explores Matlab programming with scripts and functions, vectorization, and 2D graphics.

Gitlab Agent for Kubernetes (`agentk`)

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

Numerical analysis

Covers advanced numerical analysis topics including deep neural networks and optimization methods.

Critical Data Studies: Reproducibility and Renku

Explores the significance of reproducibility in data science and introduces Renku, a platform for managing data-driven projects.