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Closing the Door on MICA

Writer's picture: Zane WolfZane Wolf


This blog is tricky to write. I drafted it a few months ago as '2024 Update Part 2 - MICA'. Here's how it began:


My second big update is that I decided to enroll in MICA's Data Analytics and Visualization Program. One of the biggest sources of friction with self-studying to switch careers is trying to figure out what you should be studying and how you should be spending your time. I was spending more time trying to answer these questions than actually practicing data visualization. I realized it might be worth it to find a program that would provide a solid curriculum and structure so that I could focus on actually learning and practicing data visualization instead. And it is always easier to complete work when you have external deadlines, right?


But in an update-ception: I've left the program.


It's like one of those movies that has the twist right in the first act. "You thought you knew what type of story you were getting? Think again." As your read through this blog, you'll probably start to understand why I never published the original draft.


Like any program, there are pros and cons. Ultimately, the cons outweighed the pros for me and I decided it was not worth the time, money, and energy to finish the program. I do believe this program can be useful, but from my experience and discussion with my peers, I'm convinced that, in its current state, this program works best for a very narrow subset of students. Let's get into it.


About the Program


Maryland Institute College of Arts Data Analytics and Visualization (DAV) program is billed as a 15-month accelerated graduate (MPS) program. It is remote and mostly asynchronous. Students are told to expect 15-20 hours of independent work time each week, and a 2-2.5hr class one day a week. This format benefits professionals who are trying to make the career switch while continuing their 9-5, and anyone who does not have access to a local data visualization program (which is a lot of people - they aren't super common yet).


There are 8 courses taught consecutively. Each one is two months long, with the final two courses focusing on the capstone project, the portfolio highlight of the program. Instructors are industry professionals who bring real-world experience to the class their teaching.





There is also a two-day virtual 'Industry Immersive' halfway through the program to network with each other, industry professionals, take workshops, and get hands-on practice with a variety of tools.


Now that you've got the lay of the land, here's my experience.


The Cons


I've very happy to say that the first class, Foundations, was taught by Amy Cesal and was a true highlight of the program. The readings were comprehensive and useful, the weekly classes were spent in discussion, doing tutorials, and engaging in breakout room activities, and the homeworks were well-designed and actually had us making data visualizations right off the bat. (This was also when I started the original blog discussing MICA and what I was thinking about it at the time.)

However, it was all downhill after that.


I could (and have) written pages detailing the specific issues I encountered with these courses and their instructors. I'm going to focus on the larger systemic issues.

'Accelerated graduate program' this was not. One would think that with 8 consecutive courses, there'd be a good overhead structure in place to ensure that each course jives together, to reduce the amount of overlap and redundancy, and to make sure the material taught goes into the depth and detail one might expect in a graduate course.


In case my slightly-snarky use of the hypothetical didn't give it away already - this was far from the case. Foundations covered the foundations of data visualization: gestalt principles, design principles, data types, chart types, style guides (one of the best assignments of the program, btw, was remaking the same chart for different audiences using three different styles, two of which were established style guides of different businesses), etc. However, each consecutive class was about 50% of the previous' classes material. The redundancy would have been okay if each course went into deeper and deeper detail and rigor on the redundant topics. Unfortunately, each instructor covered the material as if they were the first to present it, and only at superficial levels. Other students remarked they had undergraduate level courses that went more in-depth on the material than this 'graduate program.'


Rather than feeling accelerated, we all felt like we were crawling along. In circles.

'15-20 hours of independent work' was no where near the case. I probably came close to working 15 hours on the final project for a course or two. Total. Over a few weeks. Part of that is on me, and I freely acknowledge that. I could have easily put in a lot more time and effort to meet this guideline. However, me working the hours just to meet the hours is not the same as the homeworks and projects being designed well enough to justify and necessitate spending that amount of time on it.


'15-20 hours of independent work' and the 2-2-2.5hr class each week are the ingredients for a great flipped-classroom set-up. A standard class set-up includes 2-3 classes a week wherein the instructor presents information and then sends the students forth with homework and stuff to do in their other hours. A flipped classroom is a reversal of this: students spend their hours outside of class watching pre-recorded lectures, doing the reading and material learning, and then class time is spent on activities - discussions, experiments, applying principles in practice, giving and receiving feedback from the professor and each other in real time. The key point is that lecture periods are not spent giving lectures. The in-class time is way too valuable to waste it on something than ultimately can be done outside the classroom.


This structure is fantastic for course material where practical application is a major component. And also when students are limited in face-time together. On a campus, students can hang out and work on assignments outside of class. In an remote, asynchronous program....that space to hang out outside of class doesn't exist, and if it does, not in the same way.


The MICA courses did not utilize the flipped-classroom setup, with the exception of Foundations. It still had a lecture component, but half the class was spent on activities, and well-thought-out activities. The same cannot be said for the next three courses. They were heavy on (redundant, basic) lectures, the few times activities and breakout rooms were planned, they were not well-thought out and not well-suited for in-class activities, and there were very few discussions or time spent just talking with each other. We mostly sat in a zoom for two hours and listened to one person speak.


In a similar vein, homework design is a skill. It's not intuitive and it's not easy. Good homework assignments should use what was covered in class as the starting point and build not just within the assignment, but across assignments, reinforcing and enveloping the previous skills gain and tools learned into each subsequent step. Crafting good homework takes a lot of time and effort, especially in an asynchronous program like this, where all the learning is done through independent study and work.


And then the motivation to put a significant amount of time and effort into the homework wanes when you know that you're not going to get meaningful feedback on it. We only had one instructor that gave us thorough, useful feedback across the four courses I attended. We honestly lost all motivation to put in effort to courses that weren't giving us much in return.


The last thing I want to touch on is the expectation that you'll make data visualizations in this data visualization program. That was true for Foundations. That was touched on in Understanding Data (it was one class) and Data Exploration (we made some for the final project, but the class itself didn't build on the knowledge or skills directly related to making data visualizations in any meaningful way), and it was almost an after-thought in Visual Information Design. The amount of actual data visualization practice you get in the first 8 months of the data visualization program is embarrassing scant. My plan to use this program to routinely make visualizations and build a robust portfolio was utterly dashed.


Now, all that said (and all that I haven't even touched on), there are some good things to have come out of my experience with MICA.


The Pros


One of the good outcomes of the program is the community. First, Amy Cesal is actually a MICA DAV alumnus herself and founded the DVS for want of continued community and support after graduation. Participating in the MICA program has only strengthened my appreciation of and participation in DVS directly. And there's also the vast web of other MICA alumni that I have yet to truly explore but now see it everywhere, anytime I come across another practitioner and see 'MICA' in the bio or listed on their LinkedIn.




There was even a MICA alumni meetup at Outlier this year and that was a fun, learning what people are up to now after having finished the program.


But the best part, hands-down, has been my cohort. As the courses started to sour on us, we turned to each other for support and also education. I started 'Lab meeting' once a week for us to hang out, go through homework together, discuss class project ideas, etc. And it was fantastic. A couple of us with more experience actually gave tutorials and taught each other more about our areas of 'expertise.' I taught a few R workshops for the R classes, another talked about python, another walked us through their client design workflow and setup, and another gave advanced Tableau tutorials and had 'office hours' to help with final projects. It was like a little shadow-program.


A few of these relationships have even developed and now exist outside of the context of the class - which, if you've been to college, you know is the exception, not the rule. I feel very blessed about that. And having left the program, I still talk with them. I hope we never stop.


My cohort wasn't just a silver lining - it was a gorgeous, massive, peachy-white cloud rising above the storm below.


And that is the best thing this program gives you - friends to go on this journey with.


Wrapping Up


I think this program is useful for a specific subset of people: those with absolutely no design or data experience, or those simply looking to receive the degree ((which is an entirely valid reason to go through this program - sometimes you just need the bona fides, you know?). Across my cohort, that seemed to be the theme. Those that had prior knowledge and experience experienced the highest degree of frustration and exasperation with the program's shortcomings. Those that were learning everything for the first time (including no undergraduate experience), said they were learning lots and that the pace was good. More experienced prospective students wanting to dive deeper into all these concepts and improve upon existing knowledge and skills would be better suited to a more established and rigorous program.


My sojourn through the MICA program might have been shorter than I intended, but in addition to my wonderful cohort, it gave me more confidence in the skills I already had and reinforced my love of teaching. Worth it.

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