Lecture

Data, big data, clouds and IoT

Related lectures (32)

Data: Clouds and Things

Covers the fundamentals of data, cloud storage, and the Internet of Things.

Data Wrangling with Hive: Managing Big Data Efficiently

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

Distributed Information Systems: Overview and Models

Covers Distributed Information Systems, key tasks, methods, projects, evaluation, and exam support.

Introduction to Database Systems

Covers the basics of database systems, including data modeling, DBMS, data independence, and the course overview.

Data Wrangling with Hadoop

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

Model Representation: Structures and Functions

Covers the representation of mathematical models, functions, and data structures in information systems.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

Data Modeling: Concepts and Applications

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.

Knowledge Representation: Semantics and Data Structures

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Privacy: Threat Modeling and Differential Privacy

Explores the significance of privacy, risks of targeted advertising, and the concept of differential privacy.

Hydrology Modeling: Routing System

Covers the modeling of hydrological systems, focusing on the retention of floods and the example of the Routing System.

IT Security Challenges

Explores IT security challenges, Object Storage systems, website management transition, network security deployment, and project portfolio management at EPFL.

Introduction to Database Systems

Covers the fundamentals of database systems, including data modeling, information processing, and the challenges of managing large volumes of data.

Software Engineering Fundamentals

Introduces the fundamentals of software engineering, covering instruction processing, algorithms, programming languages, abstractions, testing, security, and open source.

Handling Data: Intro to Pandas

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Data Management: Overview

Introduces fundamental concepts of data management, including data models, databases, and key tasks.

Programming Bugs: Understanding, Fixing, and Preventing

Explores programming challenges, debugging techniques, software testing, and security practices in software development.

Network Inference from Textual Evidence

Delves into network inference from textual evidence, exploring information propagation, translation, and multi-input attention.

Machine Learning in Philanthropy and Non-Profit

Delves into the application of machine learning in enhancing human rights documentation and advocacy for organizations like HURIDOCS.

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