Distributed Computing: Challenges and Solutions

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

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

Introduces data warehousing fundamentals, challenges, and the innovative concept of a 'lakehouse'.

Covers data types, quantity, quality, and representativeness in the world of data.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Explores challenges and solutions in analyzing big multi-dimensional data, focusing on complex data types and anomaly detection.

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Covers the overview of information systems, data modeling, managing data, and the distinction between data and information.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

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