Data wrangling

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Data Wrangling with Hadoop: Advanced Techniques

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

Big Data: Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.

Data Wrangling Techniques: HBase and Hive Integration

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Digital Urban History: Lausanne Time Machine

Explores historical urban data from Lausanne, focusing on census information, street names, house numbers, and digitization processes.

Transformations of Input or Output

Covers handling missing data, feature engineering, and output transformations in machine learning.

Data Representations and Processing

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

Data formats and data wrangling with Hadoop

Explores Apache Hive for data warehousing, data formats, and partitioning, with practical exercises in querying and connecting to Hive.

Data Wrangling with Hadoop

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

Data Stream Processing: Apache Kafka and Spark

Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.

Collaborative Data Science: Tools and Techniques

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