Data Visualization Techniques: Applications and Principles

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Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Introduction to Machine Learning: Course Overview and Basics

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

Data Science Fundamentals

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.

Excel Exercise: Trends

Explores trend analysis in Excel using rent data from 1995 to 2023.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Advanced Pandas Functions

Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

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.

Data Science Essentials: Pandas, Numpy, Matplotlib

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

NABEL Data Analysis

Covers the NABEL Data Analysis Assignment, emphasizing data analysis skills and report formatting for air pollution concentrations and meteorology.

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.