Sure, glad to share an update. I’ll break this into three parts:

**Part-0: Wha?**

People (understandably) had a “Wha?” reaction to my initial post because I didn’t narrowly specify a field of study. The curse of having a wide range of interests, most of which can be explored with similar tools in math, statistics and science. I’ve narrowed to economics and finance, which is two (or N, depending on how you combine them) fields. So, narrower but not narrow yet.

**Part-1: Self-study**

My on-ramp to this at all was working through Richard McElreath’s “Statistical Rethinking”. If you’re interested in modeling real world phenomena, start there.

Prior to my initial post, I had gone back to re-learn the one semester of university level calculus I had while at school. I chose Morris Klein’s “Calculus: An Intuitive and Physical Approach (Second Edition)”and supplemented it with James Stewart’s “Calculus”. I could read the former (mostly) straight through, and found the latter better as a reference. I’ve supplemented further by learning how to partially differentiate equations with multiple variables.

At about the time of my post, I’d started learning linear algebra. I’d done practical work with matrices and vectors, but had never studied linear algebra itself. I found Gil Strang’s MIT Open Courseware lectures, which are available on YouTube, to be a good way to begin. I supplemented it with Sheldon Axler’s “Linear Algebra Done Right”, which is good, but is still above my level.

Also at about the time of my post, I’d written Petre Caraiani to ask him about his book “Introduction to Quantitative Macroeconomics Using Julia”. Petre provided a two very very helpful suggestions:

He suggested that I start with “Intermediate Macroeconomics” by Eric Sims and others, which is available for free here. This book is, for me, at the “Goldilocks Just Right” level. It’s intended for an early graduate school student, It is quantitative and the math is at a level of derivatives, integrations and partial differentiation that my earlier study prepared me for. I’m about 40% of the way through the book.

He suggested that I continue onto Fundamental Methods of Mathematical Economics, by Alpha Chiang, which I’ve ordered but not yet received.

In addition to the Bayesian modeling mentioned above, I’m also interested in models expressed in linear programming. So, I’ve purchased but not yet read “Optimization Methods in Finance” by Gérard Cornuéjols and Reha Tütüncü.

Truth be told, this is a subset of the books I’ve picked up, but this is the set that’s helped the most (or which I expect to help the most when I get to them).

**Part-2: Professional**

I have a non-academic background that has some skills (software engineering, data, machine learning) that are applicable to the finance industry. So, I’m changing jobs to use those skills at a company in the “asset management” segment of the finance industry.

**Part-3: Graduate Program**

I intend to start a graduate program in either the fall or in January. I have not determined which program. I am informed (by people in this thread, among others) that most economics graduate programs are to earn a PhD. I haven’t surveyed the options too deeply yet. In a cursory check, I found a number of programs that were intended for people who want to make a career change to, say, data science. For me, after a lifetime of experience, I want something a little more academic.

HTH.