Computer Engineering

<p>I have a question about the math required to become an engineer. I have always excelled in math. However, the last math class I took was college algebra almost 4 years ago. And to be completely honest I'm not sure I can recall much from that class despite passing it with an A. My question is if I decided to major in computer engineer or for that matter any engineering how much of a disadvantage would I be at.</p>

<p>Its not clear if you have completed all the required math, because you mention taking college algebra.</p>

<p>If you still have to complete the calculus sequence, dont worry about it. If you have already completed it, you might have to work a little harder in the classes, and do some independent review.</p>

<p>While you might have to work a little harder than other students, if you are motivated it wont be much of a problem.</p>

<p>You should go in with at least Precalculus (College Algebra + Trig). If you don't then it will typically take longer than 4 years to graduate. I think that coming in as a freshman with calc I+II under your belt will make things easier if you need to take physics in your first year.</p>

<p>Well have you taken anything higher than college algebra? For computer engineering you at least need to be in Calculus 1 in order for you to complete your physics courses or else it will take you longer to graduate if you haven't gotten if your math is high enough.</p>

<p>Hmm so when you return to college, you will be thrown into pre-calculus. Unless theres a placement test and you don't do so well on it, you will probably be back in college algebra.
I would recommend buying a college algebra level book and refresh your knowledge...</p>

<p>In any engineering major you will take Calc 1 your first semester. In order to take this class you don't need to have any previous calculus knowledge. But you will need a good understanding of algebra and trig. You should be able to do well knowing only the basic trig identities but you will NEED a good knowledge of algebra to do well in calculus. I'm in Calc 4 now and I still seem to make more algebra mistakes than actual calc mistakes. </p>

<p>So to answer your question you will need to have a college level algebra class before entering into calculus. Your college might provide a class to prepare you but it will push your math classes back a semester.</p>

<p>If you want to watch calculus I, II, III videos, you can find them here: </p>

<p>UCCS</a> | Department of Mathematics</p>

<p>Note that registration is required. Son found physics I, II already having studied Calc I, II, III, Linear Algebra and Probability and Statistics. Sometimes the professors forget that the class hasn't had more math.</p>

<p>Computer engineering has little to do with math. It's more logic and computer programming like.</p>

<p>Logic and computer programming are math.</p>

<p>Ok, I guess less of what most people think that math is. Less calculus, algebra, and probability, and more logic-ish stuff.</p>

<p>Calculus, algebra and probability are used in computer engineering.</p>

<p>MIT</a> OpenCourseWare | Electrical Engineering and Computer Science | 6.042J Mathematics for Computer Science (SMA 5512), Fall 2002 | Home</p>

<p>That's discrete math, not calculus nor algebra.</p>

<p>And as for probability, I would say it's not used from personal experience.</p>

<p>"That's discrete math, not calculus nor algebra."</p>

<p>1) I believe that summations are taught in Calculus II.
2) Combinatorics is taught in algebra and is used in discrete mathematics. Abstract Algebra is taught in some discrete math courses.
3) Probability: computer engineering is pretty wide-ranging. Quality control, expert systems that use sampling values for decision-making, the use of magic numbers in database query optimizers, etc.</p>

<p>Whelp, no point in arguing this any further.</p>

<p>You should repeat college algebra. You'll use algebra all the time for calculus.</p>

<p>I've encountered plenty of uses for probability in my career in CS, although part of that is because my areas of interest call for a fair amount of probability. The project I most enjoyed working on, implementing a fairness queuing algorithm for routers, was based on a probabilistic model of the traffic flowing through the routers.</p>

<p>It's also seen a resurgence in machine learning and its applications, such as search engine algorithms and fraud detection.</p>