Introduction to Data Science

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Page 3 of 4

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Machine Learning Basics

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

Critical Data Studies: Reproducibility and Renku

Explores the significance of reproducibility in data science and introduces Renku, a platform for managing data-driven projects.

Elements of Collaborative Data Science

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Principal Component Analysis: Eigenfaces

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Introduction to Renku

Introduces Renku, a platform for collaborative data science, emphasizing reproducibility, shareability, reusability, and security.

Decision Trees: Classification

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Advanced Pandas Functions

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