I completed MIT’s DEDP MicroMaster’s program— here’s a few things I learned
I took all 8 courses in the online Data, Economics, and Design of Policy program while working as a product manager.
A few years ago I was scrolling through Bill Gates’ blog looking for book recommendations when I came across Poor Economics, by Abhijit Banerjee and Esther Duflo.
“Interesting” I thought, “here are two economists using data to fight poverty… I wonder how they do it.”
I did some additional Googling and found that they had recently won the 2019 Nobel Prize. Intrigued, I Googled a bit more and found that they had also recently launched an online program— a MicroMaster’s in Data, Economics, and Development Policy (DEDP).
The timing was good— I’d been looking for some opportunities for further education that I could do while working. The online, asynchronous program would fit my schedule, and looked relevant and interesting. Plus, it was free to try, so if the courses weren’t for me, I wouldn’t be losing much.
I decided to give it a go. And who would’ve guessed how that seemingly small decision would, years later, lead me to where I am now, living in Boston and studying at MIT, working to attain the full Masters credential.
The program has had a profound and positive effect on my life, and I am very glad to have taken it. But it has not all been easy, and there are many things I would do differently if starting again today. I wanted to compile some of these things I’ve learned here, in the hopes that they help other similar learners have a fantastic experience with the program.
Context
I’ve taken all 8 online courses. I took the proctored exams and was offered a seat in the 2024 Cohort of the Masters program at MIT, where I am currently living and studying.
For context, I starting taking the program in January 2020 while working full-time as a software product manager. I had a business degree with minimal prior economics experience or education.
Program Overview
- Students must complete at least 5 of the 7 available graduate-level courses delivered online via pre-recorded lectures, readings, homework, and exams. More here.
- Course pricing is income-based. At the time I took the program, prices and ranged between $250 and $1,000 US dollars, but are likely to change over time. You can also take them free if you don’t want to take the proctored exams, which are only necessary if you want the verified certificate and credit towards the optional on-campus Masters program extension.
- Content is delivered via 3 hours of pre-recorded lecture videos each week. Lectures are divided into 5–10 minute segments separated by 1–3 question quizzes (“finger exercises”).
- Homework must be completed each week, but can be done at your own pace during the week. You can also work up to 3-weeks ahead of schedule.
- There are open-book final exams, plus proctored final exams for credential-seekers. Grading is heavily weighted towards finals.
My Experience
I tried to complete all courses in one semester, but quickly abandoned that idea. The sheer volume of material was overwhelming and it was very difficult to follow the advanced courses without doing the earlier ones first.
In the end, here’s how I approached it:
- Feb — Apr: Microeconomics + Challenges of Global Poverty
- May — Jul: Data Analysis for Social Scientists
- Sep — Nov: Political Economy + Randomized Evaluations
- Feb—Apr: Foundations of Development Policy
- Much later (for fun): Good Economics for Hard Times
- Even later (for fun): Microeconomic Theory and Public Policy
I ended up taking Randomized Evaluations, Data Analysis for Social Scientists, and Challenges of Global Poverty each 2 times, Microeconomics 3 times.
I spent about 4–6 hours per week per course. Usually I would watch lecture videos in the evenings during the week and do homework on the weekend. I found the homework took about 1–3 hours for each course.
Where it got difficult for me was when I would run into a problem where I was unfamiliar with the foundation mathematics. For example, when taking a partial derivative in Microeconomics or an integral in Data Analysis for Social Scientists. In these cases, I would try other sources, usually Khan Academy or YouTube, but these were often long, multi-day detours to address major gaps in my foundational understanding.
As an aside, I might’ve dropped out of the program had I not taken The Challenges of Global Poverty first. I was mostly interested in applications— how can this knowledge be used? And that course was full of great examples and stories, and light on math. But it contained enough math to get me interested in the topic and willing to put in the time to learn it.
Taking the Final Exams
Since completing the program, I’ve received comments and messages to the effect of “what are the final exams like?”. Makes sense— anyone who wishes to get the credential or apply for the on-campus Masters program extension is bound to be curious about what the exams are like and how to prepare.
If I had to describe the final exams succinctly, I would describe them as “tough, but fair”. They are generally going to try and assess if you understood the course material and not try to trick you. But they are not intended to be freebies or easy, I studied a fair amount for each.
What topics will be tested?
In general, exam content was very similar to problem set and finger exercise content. Any topic covered in the lecture or problem sets would be considered fair game. All other topics would not be on the test (no unfair “every economist should know this” sorts of questions).
There are no questions relating to R programming that I can recall. This is mentioned in the courses, but re-iterating here because it’s a common fear.
How tight is the time constraint?
Pretty tight if you are slow at math. If you are like me, with your math skills accruing rust from years of disuse, you may take some time to solve a math problem. This was a BIG problem for me, and resulted in numerous retakings of final exams. Key here is not just knowing how to do the math, but being able to do it quickly and accurately.
If you can do math fast, you should be fine for time.
How did you study?
Initially, I studied by re-doing the homework and finger exercises. This, kind of worked, but something that I found was that I ‘remembered’ the answer from doing them months ago. It was somewhat subconscious, but it was possible to get those questions ‘right’ without really having understood the material.
I was in this program for 3 years. By the end of the third year, I had changed how I was studying. I was getting the STRUCTURE of the homework problem, but then finding examples mainly on Youtube.
What Youtube channels did you find helpful?
What Would I Do Differently Next Time?
1. Take AP Calculus AB on Khan Academy before Microeconomics
One of the reasons I ended up re-taking so many courses was that they were really hard without a strong mathematical foundation. I have talked to other program participants and heard similar stories— let’s be real, many of us do not use the math we learned in high school or university in our day-to-day jobs, and “if you don’t use it, you lose it” as they say. I managed to squeak through with help from YouTube and Khan Academy, but decided to retake them to improve my understanding and test scores.
If I could do it again, I would complete Khan Academy’s AP Calculus AB course before taking Microeconomics. Microeconomics required taking partial derivatives which I hadn’t done before. I had taken Calculus in high school 10-years ago and Principles of Microeconomics in university 9-years ago, but had forgotten much of the content and never taken partial derivatives before. I got stuck and ended up learning mid-course via YouTube— not ideal.
Additionally, I took the Partial Derivatives section of the Multivariable Calculus course on Khan Academy. The rest of the course looks helpful as well, but I didn’t take it due to time constraints.
2. Take Statistics and Probability on Khan Academy before Data Analysis for Social Scientists
I ended up taking Statistics and Probability in the 2-weeks between the end of the course and the proctored final exam, which saved my exam grade but was hectic. I had taken Business Statistics I & II in university, but the course required a much stronger understanding and ended up being the hardest part of the program for me.
3. Take Learn R on Codecademy before Data Analysis for Social Scientists
Data Analysis for Social Scientists was for me the hardest course by far. Part of the reason was it started off immediately with both math-heavy probability and learning to code with R. Learning both quickly was hard for me. Going in with some R programming experience would’ve probably saved me a lot of stress.
The Codecademy course on R is great. Very inexpensive and easy to follow. Lots of worked examples and very relevant for the sort of data analysis performed in this program and in many of the jobs that may follow.
4. Study a LOT before proctored final exams
I took Microeconomics 3 times. The first time, I bombed the final exam, completely running out of time. I had gotten into the habit of working through the problems slowly, which was totally fine and possible due to the asynchronous nature of the program. But the exams had a tight time restriction— there was really no way to get through it without very quick answers to each question.
I took the Microeconomics a second time, and bombed it again. Why? This time, I had a much better understanding of calculus and microeconomics. But I had taken a work trip for a couple weeks and not studied. The answers were just out of reach, they’d migrated out of my short-term memory and I once again ran out of time.
The third time, I tried to learn from this, and studied like crazy right up until the exam, doing a few practice problems even right before as warm-up. It paid off, and I got 97% on that exam— but I wish it hadn’t taken me so long to do this. The exams didn’t just require understanding of the material, they required speedy, almost reflexive understanding of the material.
Final Thoughts
Prior to this program, I’d never taken an online course or studied economics. I was looking for something that would help me be more effective at work, seeing through complex problems, measuring outcomes, and predicting what will work.
I considered the course to have been a success in all of these areas, and would absolutely do it again. I recommend it highly. The opportunity to access world-class lecturers for free, on a flexible schedule is really quite an incredibly opportunity and one I am very glad MIT has made available.
Last non-trivial update Jan 27, 2024
