Data Science Essentials

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Related lectures (31)

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.

Data Visualization: Excel Charts

Covers the basics of creating and customizing charts in 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.

Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Introduction to Data Stream Processing: Concepts and Applications

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

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.

Urban Metabolism Analysis

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Linear Regression: Introduction and Training

Covers linear regression training to find the best line for given data points, essential for predicting house prices.

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