Digital Urban History: Lausanne Time Machine

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Introduction to Spark Runtime Architecture

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

Data-Intensive Applications and Systems: Overview

Covers the exponential growth of data, challenges in processing technology, data variety, cleaning, approximate query processing, multi-query analytics, and hybrid transaction processing.

Data Representations and Processing in Machine Learning

Covers data representations and processing techniques essential for effective machine learning algorithms.

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.

Query-driven Cleaning: Denial Constraints

Explores query-driven cleaning techniques for denial constraints in databases, focusing on relaxation strategies and cleaning efficiency.

Data Wrangling with Hadoop: Storage Formats and Hive

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

Big Data: Processing and Dimensions

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

Do ImageNet Classifiers Generalize?

Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.

Urban Metabolism Analysis

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Data Wrangling: Transforming Data for Analysis

Covers the process of data wrangling, focusing on transforming and preparing data for analysis.