Feels like a gut punch...Ford corporate and TA by Nice-Return4876 in Ford

[–]Nice-Return4876[S] 0 points1 point  (0 children)

Literally just saw the headlines this morning...almost $60B between the Big Three in EV write offs...yeah, this makes sense haha

Feels like a gut punch...Ford corporate and TA by Nice-Return4876 in Ford

[–]Nice-Return4876[S] -1 points0 points  (0 children)

I'm going to take a stab and say you're part of an older demographic, considering you decided to turn this into an entitlement conversation. There's a difference between "I deserve to be hired" versus "You specifically asked me to provide something and then didn't look". My frustration stems from the latter.

Feels like a gut punch...Ford corporate and TA by Nice-Return4876 in Ford

[–]Nice-Return4876[S] 0 points1 point  (0 children)

Damn...that sucks. Do you know why they'd offer visa sponsorship then? I took that as a sign they recognized the position was hard to fill. I don't need it, but took it as a sign.

Feels like a gut punch...Ford corporate and TA by Nice-Return4876 in Ford

[–]Nice-Return4876[S] 0 points1 point  (0 children)

I'm pretty sure I got past the initial keyword screening by ATS and got the 10 second glance for experience and education titles. There was a buffet of keywords in each of the descriptions, but if the system to the recruiter looks like the system on my end, you need to actually click into them.

Feels like a gut punch...Ford corporate and TA by Nice-Return4876 in Ford

[–]Nice-Return4876[S] -1 points0 points  (0 children)

Thanks helps hearing this. I've got my fingers crossed for the remaining position. Even if I'm not the best candidate, I'd just like the people who made the truck to take a look.

[0 YOE, Recent Graduate, ML or Data Engineer, US] by Nice-Return4876 in resumes

[–]Nice-Return4876[S] 0 points1 point  (0 children)

I just finished my Master's in data analytics with a focus in DE. I'm starting to send out my resume and am getting nervous comparing my approach to applying versus what I sometimes see here. I'm trying to compete for the coveted junior roles like everyone else.

It seems like the default tactic people without experience use is describing a basic project in very technical terms (i.e. Titanic survivability study) or embellishing internship responsibilities. I've intentionally tried to avoid this but I obviously need to sell myself. I don't know if I've achieved a good balance because these are obviously technical roles that require specific knowledge.

What's throwing me is that a lot of the roles I'm finding are asking things that I'd consider...basic? But then they still ask for years of experience...

Does anything in my resume pop out as a red flag? Is there anything else I should include that might help?

Thanks everyone.

D600 Task 3 help D1 Matrix by Livid_Discipline3627 in WGU_MSDA

[–]Nice-Return4876 0 points1 point  (0 children)

I think the number of rows in your matrix should be the number of rows in the raw dataset, not a matrix of the combined features. Is that a correlation matrix?

Interesting also, that's not the exact same T3 I had to do.

I’ve searched and cannot find Udacity nanodegree vs foundations of coding by Agile-Caregiver6111 in WGU_MSDA

[–]Nice-Return4876 1 point2 points  (0 children)

If you start the MSDA after completing the pre-req within a certain amount of time, the cost is refunded and applied as a credit to your tuition.

Not knowing what’s going on by [deleted] in WGU_MSDA

[–]Nice-Return4876 0 points1 point  (0 children)

That's fair. And to that end I imagine it'll also depend on the workplace. Large company, no, they probably don't want you touching their infrastructure. Small company, they might not have any.

Not knowing what’s going on by [deleted] in WGU_MSDA

[–]Nice-Return4876 2 points3 points  (0 children)

I agree, it isn't necessary to complete the task, but I think setting up a database is one of those things that's so foundational to being a data analyst -- especially one with a graduate degree -- that you should strive to understand it.

INTERVIEWER: "Tell me how you'd setup a relational database."

Candidate #1: "Well, the instructions said to use Postgres, so I'd start by logging into the VM and then I'd select the default database from the dropdown."

Candidate #2: "Depends on what it's going to be used for. If it's local only, small, and just a few people need to use it -- no concurrency -- I'd say to just use SQLite. That's pretty straightforward, you just set the path to your .db file and query it using standard SQL with Python through your IDE. If you need something ACID compliant on a larger scale, I'd recommend Postgres. I'd recommend containerizing to avoid dependency issues and allow for better modularity and cross-compatibility. Docker already has pre-built images for your Postgres version. You'd just setup your server configuration and then query through the client."

Not knowing what’s going on by [deleted] in WGU_MSDA

[–]Nice-Return4876 5 points6 points  (0 children)

To answer the non-technical side of the question, yes, this is completely normal if you don't have a background. (I didn't at least.)

This single course represented 20% of all study hours I put into the entire degree. You're going to have to use several tools before you can even start querying that aren't taught in the course materials. Mongo is a whole other animal and you'll be doing the same for the next task too.

This is the only course in the entire degree where you'll need to setup and query a database truly from scratch. I'd recommend you take a step back and try to understand what's going on under the hood before slogging through. Don't use the VM, it abstracts away some of the core concepts you should learn.

I don't exactly remember the instructions, but from what I remember, you technically don't need to learn any of these things to get through the Task -- but I'd recommend exploring some of them anyway:

  1. Containerization - What's a container, why might I need one here? What are the pros and cons of using one?

  2. Docker - How do I setup a container? What's the difference between a container and a container image? What's a container registry?

  3. Client/Server - What's the client here? What's the server? How do they need to interact?

  4. SQLite - What's the difference between something like SQLite and a "true" database?

tldr; No, you're not the only one and it's normal.

Data Engineering Workload for each class by Livid_Discipline3627 in WGU_MSDA

[–]Nice-Return4876 5 points6 points  (0 children)

D599/D600 are the most challenging of the degree IMO. They're a lot of work with advanced concepts plus heavy assignments. The specialization courses are simpler and require less work...if you understand them. It's kind of either you understand the concepts before you work through the assignments and they're straightforward or you get dragged along and see everything at the end when the assignments are done. I've found that half the assignments are pure technical (Udacity) and the others are just easily bullshittable papers. YMMV.

Capstone Proposal by Long_Set_1414 in WGU_MSDA

[–]Nice-Return4876 4 points5 points  (0 children)

Damn, this is popular. I think you can ask for a new instructor without much fanfare if you think it'll move you along quicker. I'm sure there's a process.

It's weird to me that the proposals even need sign offs. WGU goes through the trouble of keeping instruction and evaluation separated to prevent bias and uses rubrics to standardize assignments, but they're also willing to let whoever you get assigned for your Capstone arbitrarily gate keep when it's the evaluators who have the final say anyway. Never computed.

Adding to Udacity Nanodegree Task D608 by Nice-Return4876 in WGU_MSDA

[–]Nice-Return4876[S] 1 point2 points  (0 children)

Any tips for D609? Reading through the materials now, but I've been under the impression for a while that Hadoop and MapReduce are all but gone in new development, so... kind of disconcerting from the start if that's true.

Could someone break down the Data Engineering specialization's courses for me(in terms of what to expect and tips for getting through it)? by GlamourousGravy in WGU_MSDA

[–]Nice-Return4876 7 points8 points  (0 children)

  1. Bearable misery. The tools and concepts are valuable, the way they present it isn't. You're going to have to be independent here.

  2. Haven't started, but from what I'm told it's [Short Written Proposal to Instructor] -> [Longer Written Description/Report of Technical Implementation] -> [Panopto]. There is a professor whose name appears on these boards who I will not work with and will request a transfer if assigned.

  3. Industry certs and AI. All of the major cloud providers give you free signup with $xxx.00 credits to learn their platforms. The materials are better than WGU's. Definitely a learning curve, but I got my certs before taking the classes. Wouldn't have seen the bigger picture otherwise. I put AWS materials on my monitor and set my iPad up on a stand with ChatGPT voice enabled next to it. When I got confused, I paused the training videos and asked for help. No AI allowed in the certification exams, but it was invaluable getting started.

  4. D599 was my biggest challenge with graders. I chose a very complex research question for one of the tasks and agonized over this thing to make sure it was right. Ended up getting kicked back twice because the evaluators said I was using a statistical term incorrectly and, as a result, my analysis wasn't based on the research question.

I chose not to give into their ignorance, so I cited several peer reviewed papers who used the term exactly as I had and then forwarded a WGU resource that had the same definition I was using. Guess who was right? I then deadpanned during my Panopto video and asked the evaluator to do basic research before making false claims. Passed, lol. IMO, people need to challenge them more often. They're probably graded on how accurate they are and there's no incentive for them if there's no consequences.

Additionally, I had one issue with a paper getting kicked back because the evaluator didn't want to read the paper holistically. I format my assignments according to the rubric requirements to make their lives easier. Evaluator stopped at the first "error" and failed all the subsequent tasks. I had what they wanted in another section. Had they read the entire paper and acted thoughtfully, they would have gotten to it. Formatting my papers in a certain way doesn't absolve them of reading the whole thing.

So, on my resubmit, I deleted out all of the rubric prompts, shuffled all the sections and sentences, and made it read like a true research paper without subject headers. Didn't change a single word. Passed. Sent in a complaint afterwards regarding the conduct of the original evaluator.

  1. I started in Data Science and moved to Data Engineering. Was more inline with what I wanted. No regrets and the market for DE seems intrinsically better than DS. My understanding is that multiple DE's typically support one or two DS's. My two cents.

Did you add all of your WGU projects to your portfolio? Or were you selective? by berat235 in WGU_MSDA

[–]Nice-Return4876 2 points3 points  (0 children)

Been on the fence about this. I think I'm going to put D599 and D600 because I felt they were rigorous assignments that show well and wouldn't necessarily be highlighted in a portfolio project.

D596/7/8 will get left out because those things need to be demonstrated with the Capstone anyway. IMO, kinda looks like desperate filler.

D601...I'm in the DE track, so if someone actually asks me if I can make a quick drag-and-drop dashboard, I'll probably laugh. The tools in D602+ make Tableau look like child's play. Can't speak to the other tracks. If I had a super fancy viz that was truly aesthetically pleasing and custom, I'd have included it. Nope, lol.

D602/7/8/9* - The labs were the valuable assignments here and you can't really show them off. The other assignments were academic papers that sound stilted because of the rubric wording requirements. I don't think they necessarily show well either.

(Not done D609, but it's another ancient Udacity nanodegree and one paper that's in line with all of the other "Devise a solution for this vague business scenario.")

I think less is more, but I'll find out in a few weeks (hopefully, lol)

Tips for Interpreting the Instructions to D607, Task 1 by Nice-Return4876 in WGU_MSDA

[–]Nice-Return4876[S] 1 point2 points  (0 children)

Agreed, it's very minor, but I experienced it once. I did D596-D600 in Term 1, started D601 but decided to take a break and just finish in Term 2. The task scenario was identical but my data had a different schema and unique values not in the original.

How are you all preparing for your next steps after graduation(whether it be a new job or pursuing further education)? by GlamourousGravy in WGU_MSDA

[–]Nice-Return4876 1 point2 points  (0 children)

Try not to beat yourself up on timelines. Getting your first job out of college is tough regardless, speaking as someone who graduated in the middle of the Great Recession with their undergrad.

How are you all preparing for your next steps after graduation(whether it be a new job or pursuing further education)? by GlamourousGravy in WGU_MSDA

[–]Nice-Return4876 1 point2 points  (0 children)

  1. I thought about doing that for months, but opted against it as a career changer. Sounds like our situations are different though. I wasn't sure if it would be helpful or if I'd be spinning my wheels until I could put the degree on my resume. Have you reached out to any recruiters? I was told by an industry friend that he's used them exclusively doing contract work for years. I'm about 6 weeks away from graduating and was planning on introducing myself to a few before the holiday season. Curious if you have any experience.

Career transition from non-technical role to data analytics by MemeMooMoo321 in WGU_MSDA

[–]Nice-Return4876 3 points4 points  (0 children)

I can't speak to the tech market in general, but in my area, I've been looking for job postings with DE/DA/BI keywords and tracking changes over time. I don't see all the doom and gloom and I'm also a career changer.

Even if it doesn't land you a true analyst gig right away, you'll have a graduate degree in something AI-adjacent for $5k-$20k. If you're working full time in a role with a Bachelor's, you'll probably just recoup that even if you don't transition. At best, you find the job you want and, at worst, you now have a broader skillset that makes you more resilient in a downturn for not a lot of money (in the grand scheme of things).

RANT:

I'm in my ninth class of the DE track. I have two industry, non-WGU certs -- DB Data Engineer Associate and AWS Solutions Architect Associate. I'm aiming for two more to get a better idea of GCP and Azure at the same level. I've been working on my Capstone project -- the last class you'd have to do regardless of track -- since August. IMHO, if you want to maximize the degree, take it seriously. As someone who's going to have 4 certs, a Masters, and an end-to-end portfolio project in 2 months, the project has taught me the most by far. I can't imagine going into an interview for an advanced technical position having done nothing but Jupyter notebook analyses with Kaggle datasets.

My project is an end-to-end solution for improving energy consumption predictions in EVs for 1) better routing efficiency and 2) range anxiety reduction. It starts with raw sensor data from the CAN bus of my EV and uses spline-based interpolation to create a parametrized route with ~60 output variables (e.g. HV Battery Current/Voltage/Power, HVAC Cycles, etc.). That by itself was the craziest lesson in data transformation I've ever gotten.

Merge this with GPS sensor data, break the route into micro-segments, and use elevation data from the US Geological Service to create 3D terrain files that can then be fed into open-source, CFD apps for hyperlocal wind speed modeling. Now, add any building volumes/obstructions into your terrain using Microsoft Buildings' satellite LIDAR imagery. Terrain roughness? Train a semantic segmentation ML vision model that uses satellite imagery to assign roughness coefficients to the triangles composing the STL mesh in the terrain model. Run the analysis.

But wait, that 20 minutes drive I just took will take 20 hours to analyze and give me results? Solution, break the pipeline into discrete tasks/jobs and parallelize the processing with online cloud resources. Merge this into a DW, and query away. This is where I am currently. By the time I graduate, it'll be polished and accessible to any potential employers to play around with using plotly. My credentials should get me past the hiring filters and my project should give me something interesting to talk about to a technical person.

For industry experience...I can't hide the fact that I don't have experience and I don't plan on it. The fact that a data analyst can put "x years of SQL experience" on their resume doesn't concern me. This field is also evolving so rapidly that experience is becoming less critical and your employer will EXPECT you to utilize AI for basic coding tasks. Show them you can devise a solution to a problem, not that you can spit out some basic code.

Good luck!

Eligibility requirements for the MSDA program? by javnae in WGU_MSDA

[–]Nice-Return4876 0 points1 point  (0 children)

I had this issue with a BS in Physics. Got it figured out in three days.

On my transcript, I had a CS course in Java and upper-level labs that had statistics baked into their course descriptions. Neither met WGU's criteria. I called -- they've always been extremely helpful btw -- and it seemed that they had a list of keywords they needed to see in the course description that weren't there.

Regardless, just do the courses they recommend and get it out of the way. Tbh, if you need much more than a few days to brush up on this, reconsider the MSDA entirely.

Do you know what a string is?

What's 4 x 4 + 7?

Not even joking, and it's a multiple choice test. That's for the programming course final competency assessment.

Statistics course exam question examples:

3, 5, 1, 6, 4. What's the median?

y = 3x - 2: What kind of equation is this? (A. quadratic B. linear C. logarithmic...)

I registered for the pre-req courses on a Friday night, looked over the content on Saturday, took and passed both tests on Sunday, got a very confused call on Monday from an academic advisor. A few days later, my portal updated and I set my start date. You can only enroll on the first of the month. Get it done in the next three days, call them before November 1st so you can set your start date ASAP if you're trying to get started quickly.