Digital Humanities: Interdisciplinary Approach to Data Science

Related lectures (31)

Page 2 of 4

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

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

Covers Python basics, Pandas, data manipulation, visualization, and machine learning.

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

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

Covers the fundamentals of data science, the scientific method evolution, the role of a data scientist, and the significance of data as the new oil.

Introduces the course on machine learning, emphasizing the importance of Python and the final exam.

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

Emphasizes the importance of data preparedness in disaster response and explores strategies for addressing information gaps and overloads.

Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.