Principles of Data Science

Catalog description

Introduction to data science, data preprocessing and exploratory analysis, data visualization, mathematical foundations for data science (mathematics/calculus, linear algebra, probability, and statistics), Python programming language, data science frameworks and ecosystems, linear and nonlinear regression, unsupervised learning, clustering methods, dimensionality reduction, supervised learning, classification methods, ensemble methods and association analysis, neural networks, introduction to deep learning, big data and Hadoop ecosystem, anomaly/outlier detection, ethics in data science.