Introduction to Renku

Related lectures (32)

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Discusses sustainable IT practices and strategies presented at the Forum SI 2024 at EPFL.

Covers the installation of pre-requirements for MATLAB tools and introduces Thermodynamics-based Flux Analysis packages.

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

Introduces Renku, a platform for collaborative data science enabling reproducibility and promoting code and data reusability.

Explores data manipulation, exploration, and visualization in data science projects using Python.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Introduces version control with Git, focusing on repository setup, committing changes, and collaboration.

Covers data visualization techniques, tools, and concepts for effective data representation.

Covers Python list manipulation and comprehension, emphasizing memory representation and mutability.

Covers conditions, loops, functions, and graphics in R programming with practical examples.

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

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