Assignments are like "fill-in-the-blank" problems... Advice on making the most out of the assignments?

Course Q&A Data Engineering Data Storage and Queries
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I’m in the third course of the specialization and have had no issues in completing the assignments. I find the courses to be comprehensive and enjoyable. But unfortunately, I don’t think I’m learning as much as I should be to actually perform data engineering jobs.

Assignments are like “fill-in-the-blank” problems where the most of the codes (setting up terraform, running aws services programmatically, etc) are provided and you just need to “fill in” the missing values per the instructions. Because the codes are already written and architectured for you, you can do the assignments without really understanding how each code file in various folders come together to function.

In the real world, data engineering would definitely be more than filling in the blanks. Is it a fair statement that data engineers would need to be able to do whatever the developers of these assignments would have done? If so, what’d be the best approach for making the most out of these assignments?

I don’t expect a single specialization to do magic and turn me into a data engineer, but **I’d appreciate anyone’s advice.

Thanks!!

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Perhaps if you go through all the files available per the assignments might be an addition to your learning from these courses!

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I’m feeling the same way! Anytime we are given a file with 100+ lines of code in which we are tasked to fill in a few blanks, I always wonder how I would write the entire file from scratch on the job…Thanks for raising this point! Curious how the rest of the course will pan out.

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If these assignments were to be “complete blanks”
instead of the “fill-in-the-blanks” and with everyone
expected to finish within a week, off the top, I am
not sure we will all be able to finish in that window…

So, although, an end-to-end data pipeline coding/
programming assignment will definitely be more
challenging and be more beneficial (close to real
world experience) that higher standard (grounds
up difficulty level) could potentially lead some of
us to find the course as advanced and dampen
our spirits (in trying to be a data engineer)…

So, my point, the current fill-in-the-blanks style is
fine for an intermediate level course and a sweet
spot between very basic intro course and something
what you mentioned above (complete blanks) …

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If you want to get more out of the assignments, you can try to understand all the code, and how it comes together. I can make predictions about what will happen if you change some code, and then test those predictions.

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I do think the course and assignments provide a good jumping point for exploration and self-study, but the 2hr time limits are an issue.What I’ve been trying to do is complete the assignment (since the 2 hr deadline adds anxiety over losing your work) and then restart the lab and go over it closely. I think the onus is on us to delve deeper. I would really like more time in the labs though, rather than having to complete the setup and code again and again. Code, one can save by downloading the ipynb and uploading it again, but it hard when you’re doing terraform and editing multiple files.

I would love to see 4 hr time limits, or failing that a way to download the work and restart faster.

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