If you're not using it already, Course Pickle is a great resource to use for tracking down open seats in classes that you want. You just need to be quick and grab a seat before anyone else does. On that topic, don't give up on getting into those courses you didn't get into during course request! People drop in and out of classes all the time, especially once the semester is about to begin. Fill the rest of your schedule with easy electives that you will enjoy. You could also take COMM 2004 if you're up to is as well. I'd recommend taking 2 math courses at most (Multi and Discrete), CS 2114, and any other GE classes you're missing (just ENGE 1216, I presume). If you have even the slightest worry about taking all three math courses this semester, don't! You're already ahead of a lot of students out there. Now is not the time to take on tasks you may not be able to handle, even if you have a lot of room to screw up. Remember, your main goal right now is to maintain a 3.0 GPA. It's definitely possible, but you would be taking 12 credit hours of math and cs, which may burn you out a bit. If you leave it for next year, you'll be stuck doing calculus along with your CS courses, which is a pretty hefty workload. If you put in the effort, your gpa shouldn't suffer too much and you'll still be rocking a 3.0 by the end of the semester since you have such a large cushion. Personally, as shitty as the course is, I would take multi this semester and power through it as best as you can. Usage of mathematical models, analytical calculations, and graphical or numerical representations of data to analyze problems from multiple disciplines that address intercultural and global challenges in areas such as chemistry, environmental science, the life sciences, finance, and statistics. Linear and Discrete are not related in any way, and they rarely use calculus, if not at all, so don't worry about the order you take them in. In fact, it's really more of a CS course without the coding rather than a math course. Discrete moves away from regular computational math and focuses more on logic-based problems and proofs. One of the easier math courses I've taken. Linear Algebra is basically doing Algebra 2 but within matrices. These include the chain rule least squares and linear regression for data fitting, including a more general study of n-space, all key tools necessary in modern data science Markov chains for applications to probability, chemistry, population dynamics singular value decomposition, which is a core idea behind image compression and other modern data-intensive work and optimization, which is central to economics and engineering alike and is the culmination of both the Calculus and linear algebra.Fellow GE->CS student here! In my opinion, multi is the hardest of these three courses (although i've never taken multi or linear at tech, so ymmv). We also build knowledge of modern mathematical techniques crucial for applications to engineering, computer science, machine learning, economics, chemistry and other fields. This leads to a thorough presentation of integration and vector integral calculus, including volumes, iterated integrals, change of variables, applications to probability, Green’s Theorem, Stokes’ Theorem, and Gauss’s Theorem. ![]() This approach leads to a more comprehensive understanding of multivariable Calculus, including the geometry of Euclidean spaces, limits, partial derivatives, and optimization, along with modern matrix decomposition methods which have become the preferred method for solving large systems of linear equations in practice. Developed in consultation with the Stanford Math Department, Multivariable Calculus offers an innovative year-long integrated treatment of Calculus in several variables together with linear algebra.
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