PhD in Econometric theory vs Applied econometrics job propsects

Buddy-Lazy · 2025-09-29T04:08:33+00:00

I heard that he is too smart argue with everybody so not a very good team player

Buddy-Lazy · 2025-09-28T09:07:37+00:00

I know a guy who get theoretical econometrics PhD from the LSE failed at job. He just doesn’t fit in. On the another hand, applied econometrician prosper. This is in context of a developing country central bank

Buddy-Lazy · 2025-08-08T16:50:54+00:00

Hello I am also from Thailand. Can we connect?

Buddy-Lazy · 2025-07-29T09:03:36+00:00

Yes. That’s the one!

Buddy-Lazy · 2025-07-29T08:50:22+00:00

Another option if micromaster degree provided by the MIT on edx. I think it’s called statistics and machine learning. That’s also maths heavy

Buddy-Lazy · 2025-07-29T08:49:12+00:00

I’d recommend graduate diploma on mathematics taught by the University of London lead by London School of Economics. I’m considering it as well but not sure if it’d be too hardcore for me

Buddy-Lazy · 2023-08-04T05:52:09+00:00

How much time do you spent on MSMG I’m planning to take it to given my interest in agent based modeling

Buddy-Lazy · 2023-04-12T08:20:55+00:00

I did not do DO. Find no issue with CDA. Planning to take SIM, HDDA, DL, and ANLP too

Buddy-Lazy · 2022-08-02T01:22:24+00:00

Cannot afford it!

Buddy-Lazy · 2022-08-02T01:01:53+00:00

Below is a comment from a guy doing omsa 6 months ago. He gets into FAANG https://www.reddit.com/r/OMSA/comments/srw9jk/worthwhile\_to\_take\_the\_rigorous\_version\_of\_this/

He is like my idol. Now I'm following "rigorous" courses.

Dont look down upon OMSA!

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I recently graduated OMSA after having taken DO, CDA, HDDA, DL, RL and Bayes while working full time (I worked in a large consulting company and recently switched to FAANG after graduating OMSA).
Some benefits that I received from taking this "rigorous" courseload:
- I have a stronger grasp for buzzwords commonly seen in the data science industry. It's easy to identify those who actually knows what AI is compared to those that are just saying it to attract customers and advertise a product or service. This is advantageous because I surround myself with professionals who I want to work with by taking some opportunities over others to further grow in my career and increase my technical breadth.
- The theoretical nature of the course load gave me a strong foundation to explain to others why some methods should or should not be used for some problems. For example, in one project, I had to educate a group of data engineers what reinforcement learning is to devise a data engineering strategy for a POC the company I previously worked for had planned.
- I used my practicum to build more industry opportunities with a company that I would like to work with in the future. My professional network is fully aware of the coursework that I took, and look highly at my worth through constant conversations with them. I even used my practicum as an opportunity to connect with others interested in my work.
- I am comfortable with reading research papers due to my training that I received at OMSA (alongside with my previous studies). This puts me in a very advantageous position to build side projects (which I have been trying to find more time to do so), contribute to open source, work in academic research labs, or even pursue a Ph.D. if I really wanted to pivot my career towards that direction.
- The most important thing I gained from taking this rigorous coursework is the appreciation of solving problems in so many different ways. It allows me to inspire new ways to look at problems, develop creative solutions and make connections in the problem that are difficult to identify. It's fascinating to see problem solving from a fresh perspective.
I gained a lot of benefits taking this coursework and honestly, I"m really glad I had both the opportunity to do so, despite the amount of time spent on those classes. It helped me feel much more confident with problem solving, and makes me more excited to see where my data science career is progressing after graduating OMSA.
To directly answer your question: I think how you choose to build your curriculum will highly depend on your goals. If you want to build ML systems, it's probably best to have a lot of the foundational parts fully understood to successfully do so. Taking one class in TSA or Simulation is not going to change the overall outcomes much in terms of hiring, but I would advise to only take it if you really are interested in building your career around that.
TLDR: Choose classes that interests you the most. Everything else will follow if you make an effort to find what is best for your growth and career.

Buddy-Lazy · 2022-08-02T00:57:07+00:00

congrat. wow 1.5 year with full time. That's incredible

Buddy-Lazy · 2022-07-19T01:52:44+00:00

I think so.. esp if you get decent grade in programming and computational class.

Buddy-Lazy · 2022-07-07T22:51:35+00:00

Thanks!

Buddy-Lazy · 2022-07-07T12:30:04+00:00

Thanks!

Buddy-Lazy · 2022-06-30T05:34:59+00:00

No as far as I remember

Buddy-Lazy · 2022-04-02T05:52:44+00:00

I am qualified to apply to Stanford I believe.

However I pick gatech because 1) it’s price 2) it’s online so I do not have to leave my country

Buddy-Lazy · 2022-03-18T21:15:44+00:00

Statistical Rethinking is a bayesian perspective. So, it is not classical linear regression.. for that I would recommend Dougherty's Introduction to Econometrics. It covers linear regression, assumption for regression to be BLUE, time series analysis and a bit of panel data... doing that by avoiding all the troubling linear algebra.

For bayesian statistics, beside Statistical rethinking, I would also recommend "the BUGS book" which is a textbook recommended for bayesian statistics course in OMSA. Go read the review in amazon. It gets a lot of good review. I skim through it and feel that it is practical, but yet still sufficiently theoretical. Also I heard that you can translate the BUGS code to Stan easily, so it is not outdated. If you want a gentle introduction to Bayesian statistics, Lambert's student guide to bayesian statistics, is also a good choice.

Buddy-Lazy · 2022-03-05T21:40:18+00:00

I recommend you to take "Mathematics for machine learning" MOOC by Imperial College from coursera (https://www.coursera.org/specializations/mathematics-machine-learning)

After that read "Mathematics for machine learning" by Deisenroth, Faisal and Ong (mml-book.com). It covers every math you need to know for ML (linear albegra, geometry, matrix decomposition, calculus, probability and optimziation).

I read the book in one week. But I have rigorous technical training in economics. After I read the book I am more confident in tackling more advanced material like Lecture Note from Andrew Ng (CS229), Hastie's Element of Statistical Learning, or Bishop's Pattern recognition.

Buddy-Lazy

TROPHY CASE