What are /r/OMSA's favorite Products & Services?

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https://www.rstudio.com/products/rstudio/download/

Follow the instructions. R first, then R Studio.

Your situations far from ideal but you're not really in trouble until you bomb the first midterm. Make sure you crush the first midterm and regularly attend office hours from here on out and you can catch up

The CSE 6040 Syllabus has a section called "How much time and effort do you expect of me?" which discusses how to get ready for Python and linear algebra. There's a few R courses on edX by Dr. Irizarry from Harvard that go from basic to more advanced.

If you have a 4.0 in the C track, then I don't think you're going to see a lot of return on time investment on any "data science" or "machine learning" certificate. CDA and HDDA are waaaayyyy past those levels. Mmmmm maybe buzz tools like a AWS or Apache would be good, but I don't have a ton of experience with them.

FCC is 'free code camp' (https://www.freecodecamp.org/). Which has open source certificates that you can work on. They're not super in depth, but the cost is right (free). And you can work on them at your own pace (like 30 minutes a day).

There are a ton of MOOCs out there if you just look for them, but good free resources as well.

For R, try running the package swirl - you practice R, in R. These instructions outline how to run it. For Python, try Codewars which gives you a series of tasks/riddles and you have to code it in Python. Start with the easiest tasks and gradually build up - you'll likely struggle at first, need to search around how to do it, but that's a lot of what the coursework is anyways!

In addition to what colonelheero said, try some grammar checker. http://www.hemingwayapp.com/ will check your writing for readability. There is also the the free version of Grammarly In addition, get some friends to read it over and offer suggestions.

Hi!

I have a Lenovo S940 Intel core i7, 16 GB of RAM, Windows 10 Home x64. Something similar to this one: https://www.amazon.com/dp/B08TV8RBMZ/

It has worked perfectly fine with my first 6 classes. So I guess you could find something for less than a USD 1.000 that works for you.

Best,

Simulation and Computational Data Analytics are good choices. I've heard bad things about CS-6400 and I suspect you could easily get the jist of that topic in some independent reading. Bayes has some bad reviews on omscentral but from what I understand, the students who are actually interested in learning Bayesian statistics found the course helpful.

Just so you're aware, ML for trading is available for free on Udacity (in case you want to free up a slot for another elective): https://www.udacity.com/course/machine-learning-for-trading--ud501

I would also like to point out that many of the OMSCS courses already do exist for free on Udacity:

https://www.udacity.com/georgia-tech

edX used to have more OMSA courses but have now been paywalled.

I guess the refresher course on linear algebra did come to fruition:

https://www.udacity.com/course/linear-algebra-refresher-course--ud953

I have a BS in math and took a python course in undergrad. Took 6501 last semester and got an A. My plan was to finish Think Python, I'm almost done, will finish it tomorrow probably. Then go through Udacity data structures and algorithms (https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513), which is a total of 14 hours of material. And go through the lectures and quizzes for 6601 that are up on Udacity. Thats my plan before class starts, but idk maybe I'm biting off more than I can chew.

I've heard a lot of horror stories about DVA so I tried to prepare by taking the course on Udacity here https://www.udacity.com/course/data-analysis-and-visualization--ud404

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If I'm not mistaken, this is the course you're all talking about?

But it was actually really easy and there was no D3, CSS, HTML, or Java. So I'm not sure how to prepare, should I just take some online courses in these subjects?

I highly recommend getting a subscription to https://brilliant.org/ ($120/yr). It has a huge range of courses, and gives you instant feedback with constant quizzes.

Personally, I started with the fundamentals and worked my way up. Algebra Fundamentals > Algebra 1 > Algebra 2 > Trig > Pre-Calculus > Calculus Fundamentals > Integral Calculus > Multivariable Calculus > Dif Eq

If you choose Simulation for your Ops requirement, then the Probability & Statistics learning path will be really beneficial as well.

So here's my thoughts on it. Andrew Ng is of course brilliant. However, it can be a bit boring at times to listen to even though I did take the course myself. I don't see that much of a difference between Udacity/Coursera/Udemy. I personally prefer more hands on free practice. So what I did to learn ML was read Intro to Statistical Learning in R, then I practiced at Hackerrank Python and SQL here, and then finished with interview prep. It does seem that at times courses are too theoretical or academic and not focused enough on preparing us for a job?

I have yet to do the final, but I took the course this semester and am sitting at just short of 100% with a typical weekly investment of around 10 hours and a bit more prepping for mid-terms. I went in with just fundamental knowledge of python - enough to run code in Jupytier notebooks, and build basic programs. The first chunk of the class does a good job of introducing various pythonic constructs such as comprehensions, so if you're comfortable with programming, this class helps bring you up to speed in the first few weeks. Language aside, I would say the harder part of the course is in some of the challenges presented for homework and mid-terms. My background is Computer Science, so I have a strong programming background and am reasonably capable when it comes to computational problem-solving and that helped a LOT. A good benchmark is to try some code wars challenges (someone else mentioned) to get you warmed up: https://www.codewars.com/kata/56445c4755d0e45b8c00010a for example.

There's a list of these on the course syllabus, I'd work through as many as you can as soon as you get access to that. I managed all of them easily enough and still found the course to be a challenge.

Isn't Data Camp for R programming, not Python?

Regardless, I'd advise Codewars to practice Python. They give you tasks not unlike the assignments where there's minimal handholding, it doesn't matter how you do it as long as you return the expected result, etc. Good way to practice Python riddles, which are essentially what CSE 6040 is all about.

If you struggled so much the first time around it might not be the best idea to take another course at the same time, but if you insist, MGT 6203 is very light if you've already taken ISYE 6501 so have a decent baseline in R. If you're good at calculus-based probability Simulation might not be too bad - but if you aren't a math whiz you might really struggle with it. It's no programming, it's basically a math class.

Hey, I'm in this course as well (paired with MGT 6754/8803) and it's my first semester in OMSA. I am experienced with R and Machine Learning. I feel like they did a pretty good job covering the overall concepts in the video lectures. Based upon my experience with R, I feel like it will get a lot easier for you once you get the hang of it.

Do your expectations line up with omscentral? https://omscentral.com/courses/ISYE-6501

also RStudio has a portion of their website covering tons of cheatsheets if you can't google-fu solutions with respect to figuring out things in R: https://www.rstudio.com/resources/cheatsheets/

In my opinion, keep on pushing with the course, I think the time consuming aspects that you're describing will get easier for you. I think first week nerves may be getting the better of you.

This was in my email tips from freeCodeCamp, this week. I haven't read it yet, but it might help.

 3 simple rules that will help you become a Git master (7 minute read): https://www.freecodecamp.org/n/pkuy9lG19

That makes sense. I attended a Georgia Tech information webinar, last week. It was helpful. They indicated that taking the MicroMasters classes can weigh favorably on an application. It sounds like they are looking at "what steps are you taking to prepare for this program". Also, many people on this reddit and slack.com as well indicate that there is advantage in taking the Micromasters.

Hi again, Found some nice comparison, but the all-in-one solution is not available yet. But notable could be a good solution if a mobile app version comes up.

Notable Comparison

Based on the reviews here it seems so: https://www.amazon.com/SanDisk-Extreme-240GB-2-5-Inch-Height/dp/B00KHRYR0U

But you'll need to check your laptop model for compatible SSD brands/models I guess.

This Thinkpad has 32 GB RAM, 1 TB SSD, and a 15.6” display. It’s $1,069. It’s the cheapest one I’ve found with that much RAM, storage, and a screen that big.

Two downsides: It’s only an intel i5 processor. Most mid-ranges have an intel i7 processor. And, it’s ugly lol looks like straight out of 2005.

https://www.amazon.com/Lenovo-ThinkPad-E15-1920x1080-i5-10210U/dp/B086MTSCLQ/ref=sr_1_1?dchild=1&keywords=2020+Lenovo+ThinkPad+E15+15.6%22+FHD+Full+HD+%281920x1080%29+Business+Laptop+%28Intel+10th+Quad+Core+i5-10210U%2C+32GB+DDR4+RAM%2C+1TB+SS...

As others have said, no need for an actual credit course. I think Strang's course is good as well as LAFF.

I also found the No BS book good as well (you can find it free online somewhere usually):

https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0992001021

This is a very good book and assumes the reader is a beginner: Introducing Python: Modern Computing in Simple Packages

You might be able to find the first edition in PDF format if you google. It has good depth but some people might find it slow for that reason.

I basically read & practiced every example program of this book: https://www.amazon.ca/Python-Finance-Analyze-Financial-Data/dp/1491945281

Just get anaconda, launch a jupyter notebook and practice those examples in the book(minimum from chap 1 to chap 10, basically anything before "special topics" module)

Is this the right book: https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370

What about this one? https://www.amazon.com/gp/product/0387848576/ref=ox_sc_sfl_title_9?ie=UTF8&psc=1&smid=ATVPDKIKX0DER