ashramsoji

I started my MS Stats program at George Washington University this semester. The first course is Mathematical Statistics: This is the first part of a two semester course in Mathematical Statistics. Probability theory is presented as a mathematical foundation for statistical inference. Axiomatic probability is introduced then some standard discrete and continuous probability distributions are presented. Joint distributions and transformations are discussed. Probabilistic convergence concepts are introduced. The only requirements for the course are Multivariable Calculus and Linear Algebra, both of which I recently completed (that is as far as my mathematical education goes). I am finding the course to be impossibly difficult, to the point where I am only able to answer certain problems because their solution is either available online or because I am spending hours upon hours on them. This is giving me serious concern because I am not sure I'd be able to answer most of the questions on a midterm or the final. I'm not talking about not getting an A- in the course, but really just not being able to answer more than 25% of a test. I feel like I can follow what the professor is lecturing and what is being explained in the book, but am at a loss when it comes to applying it. It seems like most if not all of the students in the class had Math majors or minors in college, but if that is required to be successful in the program then I am not sure why I was admitted into it. I was wondering if people have completed similar degrees or courses and could share their experience. This course is also required for the PhD students, though there are none in the class. Do most of the students just muddle along like I am, or is this something that they are not struggling with given they know so much more maths?

Thanks Mocha. I'm going to be working full-time, so an internship is out of the question. That seems like a lot of programming languages to learn on my own while not using them.......or are they used in class? Am I able to take a course like machine learning or AI without a CS background?

Thanks everyone. I'm almost done with the course and it's been pretty smooth thus far. The Stewart textbook is pretty good and finding a solutions manual was REALLY helpful. The course is really compressed so we don't have a lot of time to practice/ask questions, etc. I feel like I understand the concepts (except triple integrals in polar coordinates), but I do wish we had more time to work out problems together with the professor and do more than just learn to solve these. I think the most important thing was to remember derivatives, integration, geometry, and trig. So many of the concepts seem to rely on a solid understanding of integrals, areas, and spaces.

When you say data science do you mean big data/analysis? What kind of courses would one take if they wanted to learn machine (ha) learning?

Piggy-backing on this: does anyone have a good resource on salary info? I'm getting my MS Stats and in trying to decide what to specialize in, it would be good to have a sense of what kind of specializations are really hot right now (or in the next 2-3 years, I suppose). I've tried things like Glassdoor, but that's not the clearest way to get at salary info. Just wondering how others decided how to focus their own specializations, either with their MS or PhD.

Can you be more specific in terms of what you meant by basic algebraic manipulation? Are we talking about things like square roots and exponents?

Thanks all. Looks like I have a pretty busy weekend ahead with polar coordinates and series. I'm a little bummed because the MIT lectures only do one class on Polar Coordinates and I'd really like to learn more about it in an efficient fashion.

Have you taken LA and Calc III? Real Analysis is going to be really difficult without it.

Well i have no idea what those are but I certainly will!

For the record, I'm pursuing a MS in Statistics starting in the Fall, and I am trying to clear some of the pre-requisites. I don't intend on taking any more of the standard math courses after MVC and Linear Algebra, though I'm sure I'll have my hands full with probability and math statistics.

Thank you, this is really helpful. I feel really solid on integration except the trig substitution (too many weird identities and integrals). I think I'll prioritize this weekend with the infinite series and polar coordinates.

Thanks for the replies! I'll be using Stewart's version http://www.prioritytextbook.com/products/Calculus%3A-Early-Transcendentals-%287th-Edition%29-Stewart.html I haven't really taken linear algebra yet although I'm familiar with determinants and matrices. Is there anything from the calculus sides of things that you all would recommend I be really sharp on?

Have you tried looking for the kind of jobs that you are interested in and what the educational requirements are? Sometimes it helps to go on Linkedin and find people who hold the position you would want and see what their background is. I think you are going to have a hard time getting an answer here because your question is so specific. That being said, it seems like you shouldn't have a hard time getting a job with a Stats MS/PhD, it just depends more on what field you want to go into.

Hi all, I am going to start a Multivariable Calculus on Monday. It's a summer course in a compressed schedule so it will be complete in just about 5 weeks -- we meet Monday through Thursday for 2 hours. I've been reviewing the lectures from MIT's OCW on Single Calc, but I was wondering what particular areas I should focus on in preparation for MVC. It's been a while since I took a Math course and given the compressed schedule and I'll be working full-time as well. I have two chapters of review left on infinite series, but I almost think I should skip those and just focus on doing a lot more integration problems. I haven't really covered polar coordinates because I didn't do that in my college Calculus course and MIT really only does one lecture on it, but if it's really important I'd appreciate a heads-up.

Hi everyone, So I was fortunate enough to be accepted into the 2 programs I really wanted to go to, Georgetown and GW. I live in the DC area and I cannot relocate to attend graduate school, and they seem like the best programs in the area for an MS in Stats (Hopkins is too far, American and Mason didn't seem too interesting, and UMD rejected me). I am trying to decide between the two. GTOWN's program is pretty new, and their classes seem very focused on teaching one "the basics" instead of letting you venture into more applied work. For example, the 10 course program has 8 requirements, so you really have room for just 2 electives, whereas GW only has 2 reqs. Some of GTOWN's reqs don't interest me, like Financial Mathematics, and I'd like my program to be structured around what I want as the ultimate goal is to land a new industry job. GW's department also seems larger and more established (not just a scion of the Math department), and the course offering seems much broader. The cost is also about the same, though GW is more conveniently located for me. This is something of a factor since I work full-time and will be attending classes at night and don't want to have to commute across town (traffic in DC is horrendous). Any advice on making this decision? I owe a response to GTOWN by midnight tonight. Currently, I'm leaning more in favor of GW.