Matplotlib and NumPy in MATLAB

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Data Science Visualization with Pandas

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

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Data Science Essentials: Pandas, Numpy, Matplotlib

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.

Digital Urban History: QGIS Practical Session

Covers the practical use of QGIS for spatial data analysis and visualization, including georeferencing historical maps and manipulating vector data.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Digital History and Digitized Press

Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.

Data Visualization: Principles and Techniques

Covers principles and techniques of data visualization, emphasizing storytelling, anomaly detection, and effective communication of results.

Urban Metabolism Analysis

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Data Science for Engineers: Part 2

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

Structuring exploratory spatial data analysis

Explores the structured approach to exploratory spatial data analysis, emphasizing the importance of analytical frameworks and the Visual Seeking Mantra.