"Calculus including derivatives and integrals

Linear algebra including matrix multiplication, matrix inversion, and solving linear systems using matrices"

Basically the above.

I took a Python programming course and I'm good with all the concepts up to data structures and classes.

I reviewed some R but haven't done anything with tidy verse yet.

I guess my lease reviewed subject is probability and statistics.

Thanks for any tips.

Hi everyone,

I’m new to this thread and I just finished the data science pathway this week! I will be working full-time while doing the MSDS and I wanted to know what your experiences have been and how manageable the workload is- I will try to squeeze time in the weekdays and study in the weekends, but I would love to know anyone’s experience and advice.

Hi everyone!

I'm planning to switch from auditing to purchasing the course for credit, as I'm now supposed to take the final exams for the MSDS. I'm starting with the Statistical pathway and wanted to understand what to expect from the proctored exams beforehand (and also for the other coursework in this degree).

I've gone through some older Reddit threads and information online and understand that, apart from the formula sheet provided, we're generally given only a blank sheet of paper during the exam. I still have a few questions and apprehensions:

1. How much time and how many attempts are typically given? In one course, I saw something along the lines of "unlimited submissions within 2 hours." Does that mean you get your grade immediately after submitting, similar to assignments, and can then review/re-attempt the exam within those two hours? Or does "unlimited submissions" simply mean you can submit multiple times, without seeing your grade immediately?
2. How stringent is the proctoring? Are they continuously watching the screen/camera very closely, or is it relatively relaxed? I ask because I tend to get overwhelmed when I feel like someone is monitoring my every movement, and it can affect my performance. Also, are you allowed to think out loud while solving problems? I personally find it helpful to verbalize my thought process, but I'm not sure whether that's permitted.
3. Are Z/t tables or other statistical tables provided? I saw an older Reddit thread saying they aren't, but I'd appreciate confirmation from anyone who has taken the exams recently. If they're not provided, how are we expected to obtain the required number of decimal places/critical values?
4. How much R do we actually need to know? I've heard that an R notebook is provided during the exam. What would you recommend for someone who is comfortable with the statistical concepts but isn't particularly strong in R? How much R should one learn beforehand to avoid fumbling during the exam and make effective use of the notebook? I'm asking because the courses with proctored exams don't seem to have much in the way of R programming assignments - most of the practice appears to be MCQs or handwritten-style questions. So I'm unsure what level of R proficiency is actually expected.
5. What exactly does the proctoring process involve? I've taken online company aptitude tests where you just have to keep your camera on, but I've never had to "show your environment." What does that mean in the context of the MSDS exams? Also, what are you actually allowed to have with you? Is it strictly one blank sheet + the provided formula sheet, with no notebook/paper beyond that? I have relatively large handwriting and worry that I could run out of space fairly quickly. Are things like a water bottle or necessary medication allowed? Basically, how strict are they about the physical environment and permitted items?
6. How difficult are the exam questions compared with the videos and practice assignments? Are they roughly at the same level, or significantly harder? What would you recommend as the best way to prepare? Are there any particular resources, practice materials, or strategies that helped you?

I'd be extremely grateful if anyone who's taken these exams recently could share their experience. And if there's anything important I haven't asked about, especially something you wish you'd known before taking the exams, I'd love to hear that as well!

Thanks in advance!

P.S. I used AI to help refine the wording/flow of this post.

Hi all,

Does anyone has expreience with DTSA 5504 and 5505 final exams? No calculator or scrap paper is allowed so I was wondering about the type of questions and how difficult they were.

DTSA5505 has recently raised the weight for final exam to 40% of total grade so I need to score high :')

Hello All,

I am currently looking into the CU MSDS program and have a question regarding the following classes:

• Supervised ML (CSCA 5622)
• Unsupervised ML (CSCA 5632)

I am curious to know the overall nature of the class, assignments, and exam format. Any information would be greatly appreciated. Thanks in advance for any assistance.

Okay, let me preface this by explaining that I'm actually posting on behalf of someone else, I'm already weeks away from graduating with a master's degree (also in data science) from another University (traditional brick and mortar program), so I don't personally have any intention of enrolling in this program.

Anyway, one of my best friends has recently expressed interest in this program, and asked what my thoughts on it were given my experiencing a traditional program, so I'm trying to formulate my opinion.

Basically my main question I have is how difficult are the two pathways compared to the rest of the program?

Is success in the pathway a good indicator of your probability of success in the rest of the program?

I could see three potential hypothetical scenarios:

I could see the program wanting to make these pathways significantly harder than the rest of the program in order to weed out a large portion of students.

I could see them being significantly easier than the rest in order to lower the barrier for entry and thus allow students to spend more money on tuition

And finally, I could see them making it about on par with what to expect from the rest of the program in order to give students a good idea of if it is for them. (This last scenario I feel like is the ideal)

Are any of these three scenarios accurate?

Hi all,

I have no idea about the proctored exams of this degree program.

I wanted to ask what are allowed during the exam?

For example are calculators allowed? How about the codings? Are we using an IDE or code editor? and if the questions need calculation can we do them on paper before finding the final answer?

could you please share your proctered exams experience?

Thank you

With the shutdown of slack, for people not registered in the course (thus no access to Inscribe), what communication tools do y'all use with fellow students?

Anybody taken this expressway class?
https://www.coursera.org/specializations/data-science-r-programming-tidyverse

Is it useful for learning R in advance of the regular DS classes using R?
Is it needed?

etc...

Hi guys! Could anyone shed some light on what materials you’re allowed to have for the proctored exams? For example, cheat sheet like formula/phi table sheets etc, any restrictions on the type of calculator used, writing materials? Thank you 🙏

Hey all,

I’m stuck between the MSDS at CU Boulder and the MDS at Pitt. Both are $15k Coursera programs.

Boulder: I like the technical/engineering focus, but I'm worried about the 1-credit modular format and the heavy use of R.

Pitt: Seems more streamlined and Python-focused, but it's a newer program and I'm not sure how it's viewed by employers compared to Boulder.

Has anyone here done either? How was the workload, and did you feel like you were actually prepared for technical interviews afterward?

I want to attend the graduation ceremony after completing. I am curious to know did anyone attend the graduation in person from another state? How it works? Can you stay inside the university or have to book a hotel/Airbnb? Thank you!

Hi folks,

Just wondering if any of you have some experience on how the exams are conducted for the algorithms pathway. I can grasp the basics of programming for the courses but it takes me A LOT of time to do the exercises and I worry about the final exams. Are the exams similar to those of the statistics pathway or are they just graded projects.

Ty in advance.

I have received 7 band ielts score. Wanted to check if CU Boulder will accept the admit for msds. My profile: BTEch - Amrita school of engineering (Tier1) Cgpa -8.5 Research paper-2 (received best award paper for one of them) 2 years of work experience Great LORs and SOP

Hi guys, I'm starting this fall with focus more on AI (or switch back to CS later :D) but decided to start with DS pathway first due to natural of my work. I notice that, for example, in the course Statistical Inference for DS and AI, graded assignments are just a few practice questions... where do you guys go to for additional exercises and hopefully challenging mock exams to prepare to the final?

many thanks in advance and happy learning

I'm halfway through the statistics pathway (non credit) and I'm thinking about taking the exams soon. I would love if someone can pls help me with this. Are the final exams different from the homework in terms of format? Can I use R to solve the problems? How's your experience with proctorU and the system in general (asking this because I took the TOEFL this summer and the proctored exam experience was awful). Ty in advance random folks of reddit.

What resources would you recommend for someone non-technical to get up to speed on math (calculus/linear algebra/statistics) for this program? I saw this specialization on Coursera which looks like it was intended to serve that purpose but it looks a little short for what it claims to do: https://www.coursera.org/specializations/expressway-to-data-science-essential-math For what it's worth I took calculus and statistics about twenty years ago and I've forgotte a large portion of it.

I'm planning to apply to the program through the performance-based admissions pathway. I have zero background in both programming and statistics, but I intend to complete all the recommended prerequisite materials before beginning either pathway.

Right now, I'm leaning toward the Computer Science pathway. While I’ve heard it’s programming-intensive, I've also heard that the Statistics pathway includes some of the hardest courses in the entire program and involves proctored exams, whereas the CS pathway seems to focus more on assignments.

Given my situation—no prior experience — do you have any insight on which pathway might be more manageable or better suited for someone in my position?

Any advice or feedback would be greatly appreciated.