Big Data: Challenges and Implications

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Collaborative Data Science: Tools and Techniques

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Data Science Fundamentals

Covers the fundamentals of data science, the scientific method evolution, the role of a data scientist, and the significance of data as the new oil.

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

ECG: HR Variability Figures

Explores heart rate variability analysis using ECG data and its physiological significance.

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.

Data Analysis to AI and ML, Social Media

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Data Privacy in eHealth Research

Explores data privacy challenges and perspectives in eHealth research, focusing on GDPR compliance, sensitive health data management, and decentralized agents.

Image Processing: Practical

Covers practical image processing using Fiji software, emphasizing data quality importance.

Ergo: ergometry figures

Covers the analysis of ergometry figures using Excel and the interpretation of respiratory and cardiovascular parameters.