Visualizing Data: Techniques and Applications

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Related lectures (32)

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Data Visualization: Principles and Techniques

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

Data Visualization: Principles and Practices

Explores data visualization principles, including chart navigation, histograms, scatter plots, box plots, and color usage.

World of Data: Visualization and Ethics

Explores data visualization's significance in communication and cognition, along with ethical considerations.

Data Science Fundamentals

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

Visualization: Techniques and Applications

Explores scientific visualization techniques and applications for understanding physical phenomena and communicating results effectively.

Visual Extensions in the Workplace

Explores the use of visual extensions, such as virtual reality and augmented reality, in enhancing workplace efficiency and communication.

Air Pollution Data Analysis

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Case Study: GIS and Participatory Planning

Explores GIS application in participatory planning through case studies in Thies and Seychelles, emphasizing the importance of simplicity and user engagement.

Data Visualization: Principles and Practices

Emphasizes the importance of data visualization techniques and practices for effective data analysis and communication.

Introduction to Machine Learning: Course Overview and Basics

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