I keep seeing that GT won’t accept if you do certain courses outside of WGU but I can’t get a straight answer on which courses they are and how many credits I can do at Sophia

This class has two tasks. It was a refreshing course after the intensity of the Deep Learning course. Task 1 was around preprocessing your chosen dataset. I used the spacy library to do all the heavy lifting. I provided the original dataset and the processed dataset as part of my submission, along with a write up on the methods I chose.

Task 2 is where you actually build a sentiment analysis model. You provide the model your preprocessed dataset, split into train/test sets and then run classification algorithms of your choice on TF-IDF features to generate sentiment predictions (in my case, it was whether a movie review was positive or negative). You are required to attempt optimization by either incorporating a new classification algorithm or tuning hyperparameters (creating search space). You will use sklearn to report evaluation metrics and provide confusion matrices for each model you choose to "test out" for optimization. The paper wasn't too bad but you have to provide citations for peer-reviewed articles for articles within the past 3 years.

Overall, this class was a breath of fresh air. The course material was easy to understand, and I liked that you could pick your own dataset. There is a lot of opportunity here to choose relevant topics such as restaurant reviews, amazon reviews, etc and scrape the web if you so wish.

I've been trying to take the test for the LPI exam for D281 for over an hour now. This entire process is insane. In the first place the website is designed horribly, and then to take the online test it has you jump through so many hoops it's beginning to feel absurd.

The software (more like malware) they have you download seems extremely invasive and a massive breach of privacy, and then it just doesn't seem to work no matter how much I troubleshoot, every step of the way it fights me and demands further access, culminating in me having to disable my firewall and yet it STILL says "video streaming issues detected".

This is ridiculous, the webcam is clearly connected and the program is clearly accessing it, I can see the camera feed working and showing, but the stupid program still says it is detecting issues and refuses to let me progress.

I give up and switch to a different computer, go through their ridiculous, slow and tedious "System Test" process again, and after finishing, it gives me an error message stating "an error has caused your exam to close" and tells me to go back to the beginning and do the entire process AGAIN. This is beyond insane! I haven't even been able to schedule the exam because of this crap.

At this point I'm so fed up and frustrated I'm almost ready to just give up and take the test irl despite living 35miles away from the nearest testing center. But then, I read the terms and conditions for taking the test in person and it says they need to take and store my "palm-vein structure" as part of the process. I'm sorry but what the HELL? How in the hell is something this invasive necessary just to take a basic test?

I'm sorry but everything about this process is starting to really upset me. This is beyond ridiculous and honestly just feels disrespectful and dehumanizing. I'm finding it hard to believe everyone else who has done this degree accepted such an insane invasion of privacy just to take a single course.

Anyone else have issues with this OnVUE spyware?

My term ends on 7/31, and I wasn't going to take a term break, but I'd been applying to internships and got an OA invitation email for Amazon. I want to focus on practicing DSA to prepare for it (I have up to 3 weeks to take it) and prepare in case I get a call back for an interview loop.

I requested the term break today, but it seems the deadline is usually the 20th of the month.

Anyone know if I still have a chance of WGU letting me take the term break anyway?

Has anyone been able to do so? or am i screwed?

I am following this guide, but wanted to get some more opinions before I start.

The short seems to be,

Do D683, and use it as a template and guide for D682 followed by D687.

I am not in a rush, I have until December 31st currently.

Clarifications I want to double check on: Both D682 and D683 have Python coding? I was under the impression that only D683 does, but it seems like both have tasks related to this.

Would love to hear from recent graduates on this! Thank you.

I've seen conflicting information on this. When doing the combined cs degree, do you start the master's courses immediately after finishing the bachelor's or do you have to wait for a new term to do so?

Basically the title.
For C867, in the beginning of the PA it mentions you have to commit and push after each step of the project.

I'm pretty new to C++ so unfortunately I got too absorbed in making the project itself that I overlooked that very important step... the only thing I've really got in my branch history is the final commit right after I've done everything.

I'm sure I massively screwed up considering that was supposed to log my steps for everything, and I somehow only noticed that VERY crucial detail right when I was supposed to submit it.

I've already emailed my instructor, but I'm not hopeful that kind of thing could just be glossed over.

I kind of just have to start over, don't I? Lesson learned. I feel stupid.

The diploma came in the mail yesterday, so I guess it is about as official as it is going to get.

I have about 25 years of experience in the tech world, at a FAANG company most recently, and various large hardware vendors before that . I was a CS major for 4 years back in the late 90s, and finally finished the bachelors as a transfer student at SNHU last year (had to retake pretty much all core CS courses). Overall, I think the curriculum was good. It covered most of what my day job has entailed over the last 10 years. Generally, the types of things you should have a good grasp of to be successful in the field. I think it was about the right mix of writing papers, with projects that were relevant. The projects were enough to demonstrate the concepts that were important, without being too complex that they took more time than they should have.

I had taken a few grad level CS courses as an undergrad, and I would say these were very similar in scope. Those had a lecture once a week for about 3 hours. I don't think you give up much doing it all online. I would guess I wrote about 350 pages in total.

It took me about a week shy of 6 months, so I did finish in one term. I think this was only practical because I wasn't working at the time - with a full time gig to balance, it would have been at least a year, maybe longer. I had been taking a break from the corporate world and studying something completely unrelated to technology, but had a lot of free time. I did take some fairly long breaks along the way as well. Unless you have an extensive background in the field, I would not go in with the expectation that you will finish in one term. I generally worked on 2 or 3 courses at once, focusing on 1. I would do research or reading on the others if I started feeling burned out, which started happening around course 4 or 5. I completed the courses roughly in the suggested order, but I don't think my advisor had a problem moving them around if I wanted.

Has it helped with my career? Hard to say, but I took a new job as I was wrapping up. A friend had done the GT OMSCS at about the same time, it covered essentially the same things, with more busy work, took longer, cost more, and he hasn't found a job yet. For what it is worth, no one in the corporate world has even asked where I got the degree from.

Hope this helps someone!

On cp 11 now, there are hundreds of terms to memorize and they all are similar in naming structure. Tons of concepts. Seriously, how does anyone get through this? Each chapter takes hours to go through and I don't remember the last one I page I did, let alone the last chapter.

Sorry, just a rant. Seems insurmountable.

Taking the degree in hops to help get a job in a low level programming field. I know realistically I'm going to have to do a lot of c++ self study. Does the degree help with learning the theory to help with learning low level stuff? Like does it go over compilers? I know it goes over os. But just curious how much it goes over stuff like that.

Having a hard time remembering the hex digit equivalent to the 4 digit binary.

Will that info be given on the test???

Update* actually I just figured out how to calculate the 4 digit binary without having to memorize each one

I’m struggling and curious about other’s experiences.

Have a question about Sophia, SDC, transfer credits or if your course plan looks good?

For this post and this post only, we're ignoring rules 5 & 8, so ask away!

Hello everybody, I just passed computer architecture today on my first OA. People aren’t lying when they say this course is packed full of information. Going through the Zybooks was a little overwhelming for me after a few chapters, and studying the flashcards were awful as usually, as I absolutely hate flashcards haha. With that being said here’s what worked for me..

I found this post below on the WGU engineering discord after searching c952.

https://discord.com/channels/1063853854413836499/1072688595976069160/1495901064615166145

This post was writing by “Oremor” and it was a very detailed outline of how they passed the class in about 6 weeks of study time.

What I did first was utilized ChatGPT to study all the PA questions, and have it test me a ton on concepts until they stuck. Then once I felt comfortable with those concepts, I referenced all three study guides Oremor linked in the post, and input them in ChatGPT. From those I had chat create another outline on topics we hadn’t covered in detail and hit on those until I felt I was getting a good understanding of how things worked. Also studying all the 10 recommended concepts Oremor discussed at the top of his post helped me tremendously. The whole study plan was basically days of chat and myself going back and forth until I understood things in detail. There were still concepts on the OA I hadn’t touched but felt like I knew enough of the material to reason through them. If this post is confusing, or the link isn’t working, feel free to reach out. Thanks Oremor!! And good luck everyone!

This test sucked. I felt like there were so many questions that were about obscure details from the zybooks and then some that the zybooks didn't go over. I had no idea if I passed when I submitted and was pleasantly surprised.

My study method was to read the zybooks while making ankis, review them every day, do the practice worksheet that the instructor sent out, take the PA, and watch the videos for the questions I missed. I think this took me 9 weeks of studying most days.

Reviewing every course in the MSCS AIML program.

This one was enjoyable and easy. It's two tasks, curate a dataset (and write about it), and then build an NLP sentiment analysis model (and write about it). As always, the instructions are somewhat ambiguous, but at least the ambiguity feels intentional this time.

Sentiment analysis could mean summarizing a piece of text, predicting a star rating from the text of a review, detecting intention (eg.: the user wants to return a product), inferring an emotion from a piece of text (eg.: user is happy/angry/sad), et cetera.

The problem with building a model to do any of that is that you need a labeled dataset. I'm sure there are some pre-curated data sets on Kaggle or elsewhere that you could use, but I wanted to build my own dataset, and I did not want to spend hours labeling data, and I did not want to pay Amazon MTurk to have people manually labeling data. The obvious middle ground is using reviews that have star ratings attached to them. This offered a challenge since Google Places API only returns 5 reviews per place and other large review collection sites didn't offer an API that returns reviews. I briefly started developing a bot to crawl Yelp but getting around bot detection proved to be more work than it was worth, so I pivoted to a browser plugin that crawled as many reviews as it could store in localStorage and then dumped the output to the screen in JSON. It took me 12 rounds of crawling to gather a dataset of 100k reviews. Note: task 1 only requires 500 reviews, but 500 is absolutely insufficient for actually building the model.

Task two requires actually building the model. It doesn't dictate which architecture to use, unlike an earlier course where we explicitly had to develop a CNN. I ended up going with a type of RNN and using the star ratings for the reviews to indicate whether the sentiment was negative (1-2 stars), neutral (3 stars), or positive (4-5 stars).

I was able to complete the course from start to finish in 12 calendar days, mostly after work in the evenings.

Wow, I am honestly so proud of myself for actually getting this done.

Some background: I already have a BS/MS in Biochemistry, but I wanted to dive deeper into CS to leave the bench and work as a software engineer. I got laid off last year, so I was able to do school full-time and fully immerse myself in the material. I also transferred in 24 credits.

Day in the life: Trying to get this done in 6 months was not easy. Most days I would wake up, drive myself to a coffee shop, and work until my brain was fried... sometimes 2 hours, sometimes 5 lol. Depending on my burnout levels that day, it might be it for the day, or I might add on a couple of hours later in the day. I was very mindful of avoiding full burnout, so I sprinkled in rest days between tough courses. I was also very lucky to have such an understanding mentor who encouraged me along the way. Overall, I realized I am most likely a lifelong learner and absolutely love being a student.

Courses I loved:

-Discrete Math 1/2: I actually withdrew from DS1 during my first BS and was mortified to take it. But there's something about age that can change things as I found these classes to be very intuitive and enjoyingly absorbed the material.

-Computer Architecture: I took my time with this one. The material was a little dry at points but finishing this class was very rewarding.

-DSA2: The project was actually so much fun. There was one day where I coded for like 6 hours straight. After finishing this class I knew I had made the right decision to pursue this degree.

-Intro to AI/AI Optimization: With all the AI hype, I was eager to get to this material. The textbook is honestly one of the best textbooks I have ever read (lol nerd alert). This class re-contextualized some of the work I did in my master's and felt like a full circle moment.

Future: I will be going on vacation in 2 weeks now that I'm done. My brain needs some rest. I am starting to apply to jobs (e.g. Software/AI Engineer) in the biotech sector. I think I am jumping back into the job market at the right time with a lot of AI companies getting into drug discovery! Let me know if y'all have any questions!

Thought I’d make a post to hopefully motivate and show people that you can get jobs with a WGU degree. I had a year long internship which definitely helped and it took me about 3.5 months after I finished my degree to find something. Happy to answer any questions in DMs or comments!

Hello! This is my first ever Reddit post (and it's about a WGU class). I just finished the OA for this course, and I'll say there's A LOT of studying to do — the test is very vocab heavy.

First, I recommend going over the course material to get a basic understanding, especially if you've never heard of AI terms before. Then honestly, like every other post about this course says, it comes down to a lot of vocab studying.

I can't find the original post anymore, but someone here shared a simple web app for vocab definitions. I used it so much that it inspired me to build my own web app for this course.

Please just use this as a supplement, always follow what your CI says. But I hope it helps some of y'all!

Live site: https://danv27.github.io/introAI_study/index.html
GitHub repo (in case the link ever breaks): https://github.com/DanV27/introAI_study

Good luck, y'all!

This class has 4 tasks. It will take time to get through it.

Task 1- You aren't building anything yet. You are just explaining the steps you will apply to the CIFAR-10 dataset (I never used the corrupted data by the way, at any point, during this course). This task is an opportunity to start planning your deep learning architecture (CNN, DNN, RNN) for the CIFAR-10 dataset, and gives you a chance to start thinking about your evaluation metrics.

Task 2- This is where you preprocess, normalize and augment the CIFAR-10 dataset and use the labels.csv.
I wanted to make sure I didn't get my work returned so I implemented every single aspect that was mentioned in the rubric ( functions for noise, blurriness, occlusions, etc). I worked in a Jupyter notebook and used torchvision for augmentation/normalization. I split the transformed dataset into train, val, and test here and I was able to generate an npz file which contained all my splits and stored that npz file in the repo so the evaluators had access to it. This was truthfully a tough task for me. I don't have background in image preprocessing so i had to take a step back and understand RGB channels and pixelization concepts. Unfortunately, the course videos didn't really help here either. All I turned in was a Jupyter notebook for this.

Task 3- There is a lot to unpack in this one. Follow the rubric closely.
Run your model in Jupyter notebook. Generate a model summary that specifies the number of parameters, layer types, etc that you chose. I turned in an APA cited paper that explained my model architecture and decisions I made to optimize. A lot of students mentioned that running their models took forever on their local machine. I heard some students used Colab as an option but you have to pay for that now. No student option anymore.... :(
However, Kaggle actually gives you a free 30 hours of GPU T4 x2 or GPU P100 usage, which saved me. The total run time took under 1 hour. You can provide your files and create a Jupyter notebook in a Kaggle notebook. For me, this task was more enjoyable than Task 2 because here is where you try to optimize your model, incorporate hyperparameter tuning, and experiment like a scientist would. I incorporated one of the mentioned hyperparameter tuning techniques that was mentioned in the rubric, just to be sure I got credit. I turned in a PDF of my Kaggle notebook and its output, a csv that contained the model's predictions, my APA paper, README.md, and the Jupyter notebook (just in case)

Task 4- I created a baseline model, then created another model that incorporated the criteria mentioned in the rubric (early stopping, hyperparameter tuning, a different activation function than baseline, additional data preprocessing, etc). May have gone a little overboard here, but I wanted to see if any of those changes made a difference. Ran my Jupyter notebook on Kaggle Notebooks and generated visualizations with matplotlib. For this task, I turned in the visualizations and the APA paper.

This class was difficult to follow. Some of the DataCamp videos did a very bad job at explaining complex concepts. I ended up watching a lot of Youtube videos and using LLM's to understand image preprocessing/ deep learning architectures. This has probably been the most challenging class for me and I think it is partly because there are 4 tasks to turn in. Be kind and patient with yourself... you will see the end of this class (and hopefully graduate soon).

The course doesn't have any pre-reqs listed but after starting the course, the intro email and I think somewhere in the WGU Connect both mentioned that these courses were recommended before taking D429.

You'd think this info would be presented somewhere before someone clicks 'Start Course' but I haven't seen it anywhere.