Data Science Fundamentals

Related lectures (31)

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

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

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

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

Explores creating art from scientific data, showcasing the fusion of creativity and scientific rigor.

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

Covers the analysis of respiratory figures and ECG heart rate data.

Covers the analysis of KMnO4 concentrations using data from a CSV file.

Introduces the Applied Data Analysis course, focusing on data science tools and project work.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

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