Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.
Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.
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.
Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.
