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ECE 461P ECE 461P. Data Science Principles. 4 Hours.
Examine principles of unsupervised and supervised learning; exploratory data analysis; feature engineering; predictive modeling for regression and classification; clustering algorithms; neural networks and stochastic gradient descent methods; scalable models for Big Data sets; case studies; and programming predictive models in Python and R. Four lecture hours a week for one semester. Only one of following may be counted: Electrical and Computer Engineering 461P, Electrical Engineering 361M, 461P. Prerequisite: The following with a grade of at least C- in each: Mathematics 340L; and Biomedical Engineering 343, Electrical and Computer Engineering 313 (or Electrical Engineering 313), or 313H; and Biomedical Engineering 335 or Electrical and Computer Engineering 351K (or Electrical Engineering 351K), 351H, or Mathematics 362K.
Bachelor of Science in Electrical and Computer Engineering
Undergraduate
http://catalog.utexas.edu/undergraduate/engineering/degrees-and-programs/bs-electrical-engineering/
The curriculum in electrical engineering and computer engineering is designed to educate students in the fundamentals of engineering, which are built upon a foundation of mathematics, science, communication, and the liberal arts. Graduates should be equipped to advance their knowledge while contributing professionally to a rapidly changing technology. Areas in which electrical and computer engineers contribute significantly are: communications, signal processing, networks and systems, electronics and integrated circuits, energy systems and renewable energy, fields, waves and electromagnetic systems, nanoelectronics and nanotechnology, computer architecture and embedded systems, and software engineering and design. Typical career paths of graduates include design, development, management, consulting, teaching, and research. Many graduates seek further education in law, medicine, business, or engineering.