Critical Data Studies: Introduction and Models

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

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

Covers Distributed Information Systems, key tasks, methods, projects, evaluation, and exam support.

Explores building a data model for scientific usecases and the significance of tracking data origin and usage.

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.

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 knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Explores models, learning paradigms, and applications in Mathematics of Data.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

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