In a data-rich, data-driven society, it is increasingly important to be able to tell a story with data. This course introduces the student to the fundamentals of advanced data visualization techniques, using both interactive computer visualizations and publication ready charts to display data and communicate model results. Whether your interests are related to business or science, effective, accurate, and ethical communication is essential in today’s data-centric world.
The fundamentals of data structures will be studied from an object-oriented perspective. Data structures discussed will include linked lists, stacks, queues, tress, sets, maps, hash tables, heaps and graphs. Concepts such as genetic types, iterators, file compression and dynamic programming will also be addressed.
The objective of this course is to teach the student the basic principles involved in the design and operation of computer networks. Topics include computer network architectures and models, physical media and signaling, data link protocols, medium access control, routing and IP, transport services including TCP/UDP, network applications, local-area and wide-area networks. The course will consist of both a lecture portion and a hands-on laboratory.
This course introduces the student to various aspects of artificial intelligence (AI), whose goals are the creation of more useful machines by making them more "intelligent." The course focuses on the fundamentals of machine learning and uses supervised, unsupervised, and reinforcement learning algorithms for classification and prediction tasks. The student will learn to build models from data using regression, logistic regression, classification and regression trees, random forests, ensemble and boosted methods, neural network techniques, and deep learning using convolutional neural networks. These algorithms are then applied to the areas of machine vision, image feature recognition, natural language processing, and general predictive techniques used in the field of Data Science.