Critical Data Studies: Reproducibility and Renku

Related lectures (32)

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Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Covers collaborative data science tools, big data concepts, Spark, and data stream processing, with tips for the final project.

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

Explores tools like Git and Docker for collaborative data science projects.

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

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

Covers data wrangling techniques using Apache Hive for efficient big data management.

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.