Data Science - Graduate Certificate

Course Descriptions

DATA602: Principles of Data Science, 3 credits. Formerly: CMSC641. An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.

DATA603: Principles of Machine Learning, 3 credits. Formerly: CMSC643. A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.

DATA605: Big Data Systems, 3 credits. Formerly: CMSC642. An overview of data management systems for performing data science on large volumes of data, including relational databases, and NoSQL systems. The topics covered include different types of data management systems, their pros and cons, how and when to use those systems, and best practices for data modeling.

DATA606: Algorithms for Data Science, 3 credits. Formerly: CMSC644. Provides an in-depth understanding of some of the key data structures and algorithms essential for advanced data science. Topics include random sampling, graph algorithms, network science, data streams, and optimization.

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