@SUNYMAN - Stevens, traditionally, has sought to educate its students beyond just their specific major or concentration. In the engineering programs, Stevens has always emphasized coursework in depth outside of one’s direct major. Most engineering problems involve interdisciplinary factors, not just a specialized or focused subject area. The high number of credits in Stevens’ ISE curriculum reflects the breadth and depth of the program, which includes much coursework in the fundamental sciences, math, and elements of computer science and theory of computation. Understanding these areas is very important to be an effective software engineer. Stevens students by and large aspire to more than just routing coding or software development, but in many cases are the architects and leaders shaping the future of their employers, products, and industries.
Most engineering programs in general require more than 120 credits, 128-130 or so is typical, but Stevens programs average 138-145 credits. Think of it as getting far more than your tuition dollar’s worth, since effectively you are getting a five year program in just four.
Industry highly values broadly trained engineers, whose problem solving capabilities extend beyond just one particular area. The salaries and career progression of Stevens graduates reflect their demand and esteem in the engineering marketplace.
Don’t pick a school just on the basis of what may seem like an easier time based upon the lowest number of credits or courses. You get what you put into school. While the 140+ credits may seem like a heavy workload, remember that it is just for your four years in school. It will pay off significantly better in the long run in your career than many of the other schools.
With respect to data analytics, yes, a knowledge of coding in the current languages (Python, R, C++, possibly Matlab, and others) is very important, but so is an understanding of the underlying theory. Any of the computer science, SWE, ISE, financial engineering, etc., programs at Stevens can provide a solid base for data analytics.