Data Science Fundamentals

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Related lectures (31)

Page 2 of 4

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

Data Visualization: Excel Charts

Covers the basics of creating and customizing charts in Excel.

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.

Handling Data: Data Models and Wrangling

Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.

Data Science for Engineers: Part 2

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

Data Visualization: Techniques and Applications

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

Principal Component Analysis: Dimension Reduction

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

Handling Data: Intro to Pandas

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Regulations: Figures of Regulations

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

Data, big data, clouds and IoT

Explores data representation, databases, cloud computing, and challenges in the cloud environment.