The official guide to Coursera certificates & specializations

[rachel83az]

Some Coursera certificates and specializations have been evaluated for ACE credit. If you've taken one of these, what have your experiences been like?

Use this template: 

Provider: (Google, Meta, IBM, etc.)
Certificate/specialization:
Content: How is the material presented? A lot of graded or practice quizzes? Are there a lot of peer-reviewed assignments or labs?
Final course format: Lab(s)? Peer-graded assignments? Graded quizzes?
Final course content vs. prior courses: Did the prior course content build up to the last course and its assignment(s), or was it a total curve ball?
Time taken: Hours? Weeks? Days?
Familiarity with subject before certificate/specialization: Never heard of it before taking the course, Low, medium, high, I do this every day.
Pitfalls, high points, things others should know: Were any of the courses in the certificate/specialization especially easy or difficult?
1-10 Difficulty level: From learning the colors to rocket surgery

[MidLifeCrisis]

Provider: Google
Certificate/specialization: Data Analytics
Content: How is the material presented? Broken up into 8 modules. All learning handled within the system.
Final course format:  Each module has 4 or 5 quizzes of 8 to 10 questions each. Quizzes aren't designed to catch you out.
Final course content vs. prior courses: There is benefit to doing the modules in order, but they are independent units. There is no obligation to go through the content if you want to jump straight to the assessments. 
Time taken: Hours? Weeks? Days? 3 solid nights. Well within the 7 days free trial. 
Familiarity with subject before certificate/specialization: Very Limited but not too much prior knowledge. Did PHP/MySQL back when that was cool. You do cover off some some very basic coding.
Pitfalls, high points, things others should know: I think the way to look at this is that it is achievable, but not easy. There are far easier ways to get generic credits if that is what you are after and as a cert within its field its not the bees knees or anything. That said if you can deal with the actual learning its a sweet way to knock up free credits, apparently it translates to 9 or 12 credits to UMPI and for me it was worth it to get that for free. The final project is a bit more involved. However you dont submit it as such, you make a declaration of sorts that you have done it. Hence I would not stress too much about it. 
1-10 Difficulty level: 5 - Difficult enough to be slightly uncomfortable with occasional wonder if you did the right thing, but nothing too over the top. Kind of like getting a tattoo.

[eminor7]

Provider: AWIT
Certificate/specialization: Real-World Cloud Product Management
Content: There are 3 courses in this specialization, each of the courses has 3 modules. Each module (except the 1st module in the 1st course) has a multiple choice quiz and a test. 
Final course format: Multiple choice practice test for AWS CCP
Final course content vs. prior courses: I found this difficult for an introductory course, especially compared to the AWS Cloud Practitioner Essentials Course on Coursera. 
Time taken: 2 Days
Familiarity with subject before certificate/specialization: I have taken the AWS Cloud Practitioner Essentials on Coursera and watched a few AWS CCP videos on LinkedIn Learning before. 
Pitfalls, high points, things others should know: The quizzes and tests were hard. The videos are a crash course on AWS products and features. I wonder how people with little experience can pass the exam within 2 weeks of finishing this course as they keep encouraging learners to do. There was very little information on Product Management. But for 6 UL this is a good deal. 
1-10 Difficulty level: 6 or 7

[AllThose299s]

Provider: IBM
Certificate/specialization: Data Analysis and Visualization Foundations Specialization
Content: 3 courses which is mostly presented as videos and 1 "capstone" final exam.

• Intro to Data Analytics has 9 graded quizzes and 1 peer-reviewed assignment.
  
• Excel Basics for Data Analysis has 6 graded quizzes, 7 Hands-on labs and 1 peer-reviewed assignment.
  
• Data Visualization and Dashboards with Excel and Cognos has 4 graded quizzes, 6 Hands-on labs and 1 peer-reviewed assignment that split into 2 parts - 1 for Excel and the other for Cognos.
  

Final course format:  20 question final exam.
Final course content vs. prior courses: The "capstone" is a 20 question final exam that can cover all the content within the course, however some questions aren't correctly marked so you could answer it correctly and the text will state it's Correct but it's marked as Incorrect so you don't get the points for it.
Time taken: About 2 weeks watching all the videos and doing the hands-on labs.
Familiarity with subject before certificate/specialization: Limited. I took a course that covered Excel like 10+ years ago, I'm aware of formulas and did some macro programming back then but I'm not that type of person who automatically opens Excel for everything - more like open a year. Taking Sophia's Introduction to Statistics was a good refresher in helping bridge the gap once the content started covering visualization, e.g. "What's a histogram"
Pitfalls, high points, things others should know: The videos & hands-on labs reinforce the content needed to complete the assignments. If you rarely or never used excel you will be able to figure things out especially with the labs. I recommend doing the assignments on a larger monitor if you can vs a laptop screen only. 

The assignments felt a bit hand-holding but towards the end of the Data Visualization & Dashboards with Excel and Cognos it is evident that the hand-holding has stopped and is purposely vague because it will tell you what type of visualization and metrics it expects and you have to obviously figure out which the metrics to set correctly in fields panel in Cognos, etc. but you won't really know if you did it correctly because it doesn't show an image of what it is expecting until after you submit it and start reviewing your fellow peers. Not enough to fail the assignments but enough that you might not score a perfect 100%.

The annoying part with the final exam (besides the occasional incorrectly marked answer) is it seems to reference (the intro to data analytics) videos with questions like "In Week [X] video titled "[Name of the video]", start of question that then references what [Person] defines / states about [Topic/ What is an example of?]". So at first there's the panic of "which course is the exam in reference to when it states the week" and then followed by with "Who?!" which thankfully they state the person's name when they first appear in that sections video and there is enough time in the final that you are able to find the information. I was expecting a much more difficult final or at least a longer final. Comparing it to other providers finals it felt more like a sophia final than a study.com final but the assignments felt slightly like study.com as far as workload / effort.

Surprisingly enough while taking the data visualization & dashboards with Excel and Cognos I was expecting the course to emphasis and promote Cognos a lot (since it's an IBM product and this course is provided by IBM) however it didn't seem like a cheesy course to push sales. Instead it covered enough that I actually would be interested in using Cognos personally to learn more of what I could do with it / how far I could push it on my own or wish that the specialization had at least another week worth of content to cover.

1-10 Difficulty level: 1-3 if you ever had to do data cleaning or any sort of data visualization and have used Excel. 5 and higher if have never heard of Excel or the most you know about computers and technology is "the google" but honestly the content and labs cover enough that you should still be able to pass.

[eminor7]

Provider: Google
Certificate/specialization: Project Management
Content: Videos, readings, quizzes, peer-graded projects. Courses 2-5 each had one peer-graded project. The capstone course (course 6) had 8 peer-graded projects. 
Final course format: Peer-graded assignments
Final course content vs. prior courses: Final course was the toughest since it had so many projects. 
Time taken: about 2 weeks
Familiarity with subject before certificate/specialization: some prior knowledge; took Sophia Project Management and watched LinkedIn Learning videos on project management before
Pitfalls, high points, things others should know: This course provided a good overview of project management.  However, for the peer-reviewed projects, the graders were like the Sophia Eng Comp graders at times (meaning one grader can fail you when they think you didn't follow the rubric while another gives you a perfect score). Brace yourself for the projects, they are not very difficult but there are many of them. 
1-10 Difficulty level: 5, maybe easier for those with experience

[spohara]

Provider: IBM 
Certificate/specialization: Data Science Professional
Content: Mostly videos (one or two readings through the whole thing).  Lab midway through a module and a quiz at the end.  Quiz is straight forward and often based on the learning check that pops up during the video.  At the end of each course there is a peer reviewed assignment (almost always a Jupyter notebook).  There is also a final quiz, which is pretty short (usually 8-12 questions) straightforward and similar to the  module quizzes.
Final course format: The final course is a series of labs that builds to the final deliverable which is an absolutely miserable powerpoint slide deck.  It is 40-50 slides of the most tedious copy paste nonsense you'll ever do.  The labs are a good check on learning and refresher for everything you did previously.
Final course content vs. prior courses: Prior content will fully prepare you for the final course.  
Time taken: Around 4 months total.  It is a 10 course cert and some are easy and some you have to slog it out.  Time wise, if you want to do a course a week, it is certainly possible.  I did a series of sprints followed by  a week or even weeks with essentially no progress.  Most courses I did finish in around a week with around an hour on each week night and around 3-5 hours each weekend day.  That's an average.  
Familiarity with subject before certificate/specialization: It varied.  I have 0 python background, but I have taken grad level courses in R and I do SQL on a daily basis, so that part was one I zipped through.  I understood the data science at a fundamental level, but had some programming to learn.
Pitfalls, high points, things others should know: The actual data science was kinda sparse and not very good.  Having an academic background in this stuff, I question some of the methods at some points in the course.  And there were also points where statistical methods were just completely glossed over.  I finished my grad school methods sequence (3 courses) and there was stuff they brought up that I had no clue about, but then they explained nothing.  As far as learning python and getting familiar (main purpose for me), I thought it was pretty good, and I learned a ton.  I do wish the course required you to write more code (they give you a lot then have you fill in the blanks in most labs).  Also don't get too hung up in the beginning on the code for the visualizations (which they give you), because you'll get to it in the course which is later in the sequence.
1-10 Difficulty level: 5.  That's my objective difficulty level.  You don't get so deep into the data science methodology or scientific method, so it doesn't get up to grad level on that front, and I had 0 python knowledge coming in.  I would say it probably isn't for someone with absolutely 0 programming background (although you could probably manage), but if you understand the basics, you should be pretty good to go.


Also on a side note, soon I should have the TESU transfer equivalencies on this.  I'm going back and looking at the CS or DS undergrad as something to do hopefully fairly quickly vs going back to grad school just yet.  By the end of this certification, you complete the Data Science Foundations Specialization, the Applied Data Science and the Data Science Professional.  Ace evaluates them as 7, 12, and 12 credits, but there is some overlap (with some slightly different wording for the course recommendations).  I guess we will see how many total credits come over.

[mohelena02]

Provider: Google
Certificate/specialization: Data Analytics
Content: How is the material presented? Broken up into 8 modules. All learning handled within the system.
Final course format:  Each module has 4 or 5 quizzes of 8 to 10 questions each. Quizzes aren't designed to catch you out.
Final course content vs. prior courses: There is benefit to doing the modules in order, but they are independent units. There is no obligation to go through the content if you want to jump straight to the assessments. 
Time taken: Hours? Weeks? Days? 3 solid nights. Well within the 7 days free trial. 
Familiarity with subject before certificate/specialization: Very Limited but not too much prior knowledge. Did PHP/MySQL back when that was cool. You do cover off some some very basic coding.
Pitfalls, high points, things others should know: I think the way to look at this is that it is achievable, but not easy. There are far easier ways to get generic credits if that is what you are after and as a cert within its field its not the bees knees or anything. That said if you can deal with the actual learning its a sweet way to knock up free credits, apparently it translates to 9 or 12 credits to UMPI and for me it was worth it to get that for free. The final project is a bit more involved. However you dont submit it as such, you make a declaration of sorts that you have done it. Hence I would not stress too much about it. 
1-10 Difficulty level: 5 - Difficult enough to be slightly uncomfortable with occasional wonder if you did the right thing, but nothing too over the top. Kind of like getting a tattoo.

I am doing this program now and I am struggling with the SQL part! Incredible to me that you did it in 3 days! I am two weeks in and halfway.

[rachel83az]

Provider: AWIT (Advancing Women in Technology)
Certificate/specialization: Real-World Cloud Product Management Specialization
Content: Some videos, mostly reading. Each of the 3 "courses" is broken down into 3 "weeks" with 1 graded quiz per week.
Final course format: Exactly the same as the other courses: 3 "weeks" with 1 graded quiz each. However, the final graded quiz is a 65-credit practice exam. For each attempt, you are given over 5 hours to complete the final exam, if you need that much time. 
Final course content vs. prior courses: It's all pretty uniform, but the final "practice exam" is a doozy. It took me about an hour to finish.
Time taken: A couple of weeks; could have gone faster, if not for procrastination.
Familiarity with subject before certificate/specialization: Despite its name, this course is focused solely on material covered by the AWS Cloud Practitioner exam. I had 0 familiarity with this specific subject beforehand.
Pitfalls, high points, things others should know: The videos are largely (though not entirely) irrelevant for the quiz contents. Skip those and focus on the provided reading material/flashcards. If you only watch the videos, you probably won't be able to pass. I doubt if you'll be able to pass the AWS Cloud Practitioner exam after just 2 weeks of study with this course, but the specialization itself can easily be finished in 1-2 weeks of dedicated study.
1-10 Difficulty level: 3-4; no real technical knowledge needed, but you do need to memorize silly-sounding terms like Elastic Beanstalk, Snowmobile, and Glacier.

[spohara]

Provider: IBM
Certificate/specialization: Data Science Fundamentals with Python and SQL
Content: Labs, Quizzes, Final exam (quiz length)
Final course format: Peer graded Jupyter Notebook
Final course content vs. prior courses: Final content is covered by previous course content
Time taken: I only had to take one additional course for this (did all the others in a previous cert) - the statistics one.  Took about 8hrs for that one.  I'd estimate about 2 weeks to do the whole thing.
Familiarity with subject before certificate/specialization: Very familiar.  By the time I got to this one all I had left was the stats, which is a strong suit for me.
Pitfalls, high points, things others should know: Most of these courses are on the easier side, but I thought the stats was actually something that might be tough for some.  I didn't love the explanations, but I did love that the stats project you build the notebook from scratch.  Much more learning reinforcement that way (other courses in this specialization write half your code for you and have you fill in the blanks)
1-10 Difficulty level: I'll go with a 6-7.  It wasn't challenging for me, but it took me months to actually learn stats at the level they're trying to teach to and I had much more intuitive explanations.  Helps to have calculated all of these things by hand in the past.  Still, you can fake the funk and get by even if you don't fully get it - just know you're always looking for p<.05 and let Python give you the answer.  Everything else in the specialization is easier.

[sarahmac]

Provider: DeepLearning.AI
Certificate/specialization: Deep Learning
Content: Coding exercises (autograded, not peer reviewed), Quizzes, Final exam (quiz length)
Final course format: Same as other courses
Final course content vs. prior courses: Final content is covered by previous course content
Time taken: Two days

Familiarity with subject before certificate/specialization: Very familiar, I "do" statistics as a research scientist and can code at a basic level. Although I wasn't familiar with many of the specific ML algorithms, I have transferrable skills and can write code to the autograder's standard.

Pitfalls, high points, things others should know: There are five "courses", but one is only two quizzes long and is based on two case studies. If you do these on the first pass through they take maybe 20 minutes tops. I recommend doing that course last. That leaves only four actual courses. All of the concepts are explained very well and I found the coding exercises fairly straightforward since there is no guessing what they are looking for. They tell you what you need to do at each step, you just need to decide how to implement it. Anyone who has taken calculus-based statistics and can code at a basic level will likely find it quite straightforward. On the other hand, someone who has only done e.g. Sophia Stats may be more likely to struggle since knowing how to implement the step is based on your knowledge of stats, matrix algebra, and vector calculus (i.e. why you need tensor addition and cannot simply use + for two vectors, or when it says initialize to zero why you need a 0 for a single variable but [] for a variable that will hold a vector). This stuff can be learned, it just will likely take longer than a couple of days to finish. I recommend reading this primer on tensors and vector operations.

I really liked the integration of the jupyter notebook into the Coursera environment. I hated the external Skills Network environment for the IBM courses. You can run everything as you go and there are no surprises when you submit to the autograder (which you can do an infinite number of times). If your code is working inside the jupyter notebook, you will get 100 from the autograder. This was the best designed course I have seen on Coursera in terms of user-centric design. Because everything is hosted inside Coursera your notebooks are saved and linked to your account. You can close things and go back later and your progress so far will still be there.

1-10 Difficulty level: I'll go with a 6 for me personally since I didn't find it challenging (although it was very interesting) and was only effectively four courses long. It requires pre-requisite knowledge though. If you don't have the prerequisite knowledge I would give it a 7. It could even be an 8, but the class is so well designed it still holds your hand through the steps and the company maintains its own forum with help boards for their classes (they also have other non-ACE certificates). The difficulty is not with the content per see, in fact the ML/AI part is kind of "in the background", but with having the right prerequisite knowledge to know how to do the coding tasks. The theoretical part and the quizzes are fairly straightforward if you watch the videos (I watched 2 x speed). I would gladly take more courses from them if their other certificates got ACE Endorsements (cannot say the same about the IBM ones, v glad they are over).