Interdisciplinary Engineering Courses
Of the following courses, some may be requirements for degree programs, and others may be taken as electives. See your departmental program of study or consult with an adviser for more information.
ENGI E1102x and y The art of engineering
4 pts. Lect: 4. Professor Vallancourt.
Core requirement for all entering SEAS students. This course is a bridge between the science-oriented, high school way of thinking and the engineering point of view. Fundamental concepts of math and science are reviewed and re-framed in an engineering context, with numerous examples of each concept drawn from all disciplines of engineering represented at Columbia. Non-technical issues of importance in professional engineering practice such as ethics, engineering project management, and societal impact are addressed. Lab fee: $350.
EEHS E3900y History of telecommunications: from the telegraph to the internet
3 pts. Lect: 3.
Historical development of telecommunications from the telegraphy of the mid-1800s to the Internet at present. Included are the technologies of telephony, radio, and computer communications. The coverage includes both the technologies themselves and the historical events that shaped, and in turn were shaped by, the technologies. The historical development, both the general context and the particular events concerning communications, is presented chronologically. The social needs that elicited new technologies and the consequences of their adoption are examined. Throughout the course, relevant scientific and engineering principles are explained as needed. These include, among others, the concept and effective use of spectrum, multiplexing to improve capacity, digital coding, and networking principles. There are no prerequisites, and no prior scientific or engineering knowledge is required. Engineering students may not count this course as a technical elective.
ENGI E8000 Doctoral Field Work
1 pts. Professor Kachani and Associate Dean Simon
Field work is integral to the academic preparation and professional development of doctoral students. This course provides the academic framework for field work experience required for the student’s program of study. Field work documentation and faculty advisor approval is required prior to registration. A final written report must be submitted. This course will count towards the degree program and cannot be taken for pass/fail credit or audited. With approval from the department chair or the doctoral program director, doctoral students can register for this course at most twice. In rare situations, exceptions may be granted by the Dean's Office to register for the course more than twice (e.g. doctoral students funded by industrial grants who wish to perform doctoral field work for their corporate sponsor). The doctoral student must be registered for this course during the same term as the field work experience.
Master of Science in Data Science
Candidates for the Master of Science in Data Science are required to complete a minimum of 30 graduate-level credits, which includes seven required courses: Algorithms for data science, Machine learning for data science, Exploratory data analysis and visualization, Probability theory, Statistical inference & modeling, Computer systems for data science, andCapstone & ethics. A minimum of three elective courses are chosen in consultation with the student's adviser should be a technical nature, 4000-graduate level course or higher that expands the student's expertise in data science.
Certification of Professional Achievement in Data Sciences
Graduate: Online delivery
Candidate for the Certification of Professional Achievement in Data Sciences, a nondegree part- time program, are required to complete a minimum of 12 credits, including four required courses: Algorithms for data science, Probability and statistics, Machine learning for data science, and Exploratory data analysis and visualization.