Knowledge Management: Disaster Risk Reduction

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Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.

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.

Advanced Pandas Functions

Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

Data Visualization: Techniques and Applications

Explores data visualization techniques, design impact, and interactive applications for effective information communication.

Data Visualization & Storytelling

Delves into data physicalization, expressiveness, feminist visualization, and the balance between exploration and explanation in data visualization.

Collaborative Data Science: Tools and Techniques

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

Introduction to Spark Runtime Architecture

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

Data Preparedness: Improving Response through Information Management

Emphasizes the importance of data preparedness in disaster response and explores strategies for addressing information gaps and overloads.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Data Science Fundamentals

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.