Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Internet of Things and the Evolved Workplace

Related lectures (31)

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

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.

Analytics on Data at Rest and Data in Motion

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

Digital Transformation: Solutions and Data

Explores digital transformation opportunities, big data, analytics, and technology innovations in business and research.

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.

Big Data: Processing and Dimensions

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

Introduction to Data Stream Processing: Concepts and Applications

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

Data Wrangling with Hadoop: Storage Formats and Hive

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

Data Wrangling Techniques: HBase and Hive Integration

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

Introduction to Data Stream Processing: Concepts and Applications

Covers data stream processing concepts, focusing on Apache Kafka and Spark Streaming integration, event time management, and project implementation guidelines.

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.

Introduction to Spark Runtime Architecture

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

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Real-time Intelligence: Data Challenges and Hardware Evolution

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

Introduction to Data Stream Processing

Covers the fundamentals of data stream processing, including real-time insights, industry applications, and practical exercises on Kafka and Spark Streaming.

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.

Spark Data Frames

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Data Wrangling with Hive: Managing Big Data Efficiently

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

Data Science Essentials

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

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.