Augmenting MBA with University's FinTech Program vs. Sub-Par Internship

Context
* I'm a 1st-yr MBA student at Columbia (CBS)
* I have a B.S. and M.S. in Engineering from a Top-20 university
* 7+ years working at a super-major oil company in a very technical SME role prior to MBA
* Secured a summer associate IB internship at a relatively weak boutique firm, which focusses on sectors that aren't related to my past experience, I have no real passion for, but don't necessarily hate.

Recently, I've been grappling with the idea of pursuing a relatively new and (allegedly) thought-leading FinTech bootcamp program that is offered both at Columbia and Rice (in partnership with Trilogy), which serves to address the widening hard-skills gap within the financial sector. After coming to terms with the fact that I don't feel like I secured anything close to an 'optimal' internship opportunity, I started researching the 24-week program, which covers the financial hard skillsets that I feel would be beneficial for me - having had no previous experience in anything close to finance. I also happen to have a bit more passion for learning core skills.

(2) caveats before I pose the ultimate question: 1) I want to do what's best for my career above all else, even if that means doing an internship at a sub-par firm; 2) I don't know how real the skills gap is - as it pertains to AI/Machine Learning, Python/R, Programming, etc. (i.e., hard skills) - because I have not functioned in that world. I've been in a super technical Reservoir Engineer. So, I would very much appreciate your objective assessment on the value of working toward learning these things that will be a big part of the finance/investment industry in the near future.

My posed question to the forum is: considering my background, my current situation, and COVID destroying the world right now -- might forgoing the internship to build up a hard skills arsenal be the smarter play? Details on the curriculum below.

Curriculum:
* financial fundamentals (advanced excel modeling, time-series analysis, financial analysis/ratios)
* financial programming (Python, Pandas, Matplotlib, API interactions, AWS, and SQL)
* financial libraries and tools (NumPy, SciPy, Ffn, Quantopian)
* blockchain and cryptocurrency (Ethereum, smart contracts, consensus algorithms, transactions, etc)
* machine learning applications building in finance (algorithm trading, random forests, k-means clustering, support vector machines, linear regression, Scikit-learn, financial modeling, forecasting, and logistic regression)

Thank you in advance.