Lectures related to Data Preprocessing

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

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

Explores water consumption data in Geneva, including charts on consumption and losses, available datasets, and data processing phases.

Introduces Apache Spark, covering its key features, history, RDDs, architecture, and distributed computing framework.

Explores spacecraft data processing systems, architectures, and network requirements.

Covers digital history projects, social relationships in research, data preprocessing challenges, and network analysis tools.

Covers the application of concepts in measurement and quantitative analysis.

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

Covers streaming algorithms, power of two choices, Misra-Gries estimator, and AMS sketch for frequency estimation.

Delves into the role of bots in Wikipedia, their wikification of public domain content, and the controversies surrounding their use.

Explores data representations, histograms, neural networks, and deep learning concepts.

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

Explores the relationship between window length and model performance in machine learning.

Explores deanonymization using public datasets from Netflix, focusing on matching users and evaluating films based on ratings.

Covers the processing of data from a chemical experiment using Excel.

Provides an overview of the Apache Spark ecosystem, covering basics, operations, and key components.

Showcases a digital humanities student's internship experience in trading intelligence and data analysis.

Covers neural signals, brain enthusiasm, neuroimaging, and statistical analysis in neuroimaging studies.

Explores computer vision for global biodiversity monitoring, addressing wildlife decline and the challenges of automating data processing.