08-21-2022, 11:46 PM
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.
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.
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