Large-Scale Data Commons: Venice Time Machine

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Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course, focusing on data science tools and project work.

Introduction to Machine Learning: Course Overview and Basics

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

Urban Metabolism Analysis

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

Introduction to Matplotlib

Covers the basics of Matplotlib for 2D graphic representation in Python, including plotting data and visualizing functions.

Big Data: Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.

Data Science Essentials: Pandas, Numpy, Matplotlib

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

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.

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.

Data Visualization: Principles and Techniques

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

Data Visualization: Excel Charts

Covers the basics of creating and customizing charts in Excel.