Lectures related to Linking IoT sensors and cloud

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 Analysis: KMnO4 Concentrations

Covers the analysis of KMnO4 concentrations using data from a CSV file.

Biomechanics: Velocity Analysis

Explores the analysis of velocity data in biomechanics using Excel for visualization and interpretation.

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.

Headline Analysis: Language Impact & Success

Explores the influence of language on headline success through real-world data analysis and statistical testing.

Digital History and Digitized Press

Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.

Digital Transformation: Solutions and Data

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

Regulations: Figures of Regulations

Covers the analysis of ECG heart rate data and respiratory flow measurements using Excel.

Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.

LabVIEW: Data Processing and Visualization

Introduces LabVIEW for data processing and visualization, covering topics like waveform synchronization and color lookup tables.

Introduction to Data Stream Processing: Concepts and Applications

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

Data Preparation for Machine Learning: Categorical and Numerical Data

Explores data preparation for machine learning, emphasizing categorical to numerical conversion and effective data visualization techniques.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Digital History Projects: Design and Analysis

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

Data Representations and Processing in Machine Learning

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

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 Wrangling Techniques: HBase and Hive Integration

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

Data Virtualization Demo: SmartDataLake

Showcases a demo on adaptive data virtualization in SmartDataLake, focusing on assembling company profiles and executing join queries across datasets.

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.