Building DH Portal: Data Platform for Researchers

Related lectures (31)

Covers data representations and processing techniques essential for effective machine learning algorithms.

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

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

Covers fitting linear regression on features of the original predictors for flexible feature representation.

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

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

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

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Covers the presentation of the equipment key users module and its focus on planning, data loading, and integration tests.

Covers data representations, challenges of imbalanced data, and strategies for data normalization and cleaning.

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

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