Data Preparation for Machine Learning: Categorical and Numerical Data

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Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

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

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

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

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

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

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Covers data visualization techniques, tools, and concepts for effective data representation.

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