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Concept

Data mapping

Related lectures (32)

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.

Introduction to Data Stream Processing: Concepts and Applications

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

Introduction to Data Stream Processing

Covers the fundamentals of data stream processing, including tools like Apache Storm and Kafka, key concepts like event time and window operations, and the challenges of stream processing.

Real-time Intelligence: Data Challenges and Hardware Evolution

Explores data challenges and hardware evolution for real-time intelligence in the era of big data.

Data Wrangling with Hadoop: Advanced Techniques

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

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 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 Wrangling Techniques: HBase and Hive Integration

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

Collaborative Data Science: Tools and Techniques

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

Elements of Collaborative Data Science

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

Data Wrangling with Hadoop: Storage Formats and Hive

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

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Data Wrangling with Hadoop

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

Big Data Best Practices and Guidelines

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

Data Visualization: Principles and Practices

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

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

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

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.