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Concept

Data transformation (computing)

Related lectures (31)

Data Modeling: Concepts and Applications

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Data Wrangling and Analysis

Covers a homework assignment on data wrangling and analysis using Python's pandas library for real-world datasets.

Data Wrangling with Hive: Managing Big Data Efficiently

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

Nexus Forge: Building and Using Knowledge Graphs Made Easy MOOC: Simulation Neurocience

Explores Nexus Forge, a tool for creating and utilizing knowledge graphs effortlessly.

Data Wrangling with Hadoop: Advanced Techniques

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

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 Techniques: HBase and Hive Integration

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

Data Wrangling with Hadoop

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

Collaborative Data Science: Tools and Techniques

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

Data Wrangling with Hadoop: Storage Formats and Hive

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

Elements of Collaborative Data Science

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Introduction to Data Stream Processing: Concepts and Applications

Covers the principles of data stream processing and its applications in real-time data analysis.

Data Visualization: Principles and Practices

Emphasizes the importance of data visualization techniques and practices for effective data analysis and communication.

Advanced Spark Optimization Techniques: Managing Big Data

Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.

Handling Data: Data Models and Wrangling

Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.

Data Science in Personalized and Global Health: Privacy-Enhancing Technologies

Delves into data science in personalized and global health, emphasizing privacy-enhancing technologies and AI applications in healthcare.

Introduction to Spark Runtime Architecture

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

Digital Urban History: Lausanne Time Machine

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