Although it's not directly related to webdev, I highly, highly recommend the Coursera course Learning How to Learn as a starting point: https://www.coursera.org/learn/learning-how-to-learn
For the computer side of things, I highly recommend Harvard's CS50, which is completely free, for an introduction to computer science . It has a great subreddit  and is a fantastic resource. MIT also offers a great pair of free introductory classes on edx. 
There are so many variables and so much luck involved that there is no guaranteed path, but these are two great resources to get started. These were some of the resources I used to transition from no-CS (disclaimer: with a physics degree but zero programming experience) to a programming job at a startup. I've since continued learning through online and in-person classes and joined a large tech company.
Happy to answer any questions about these resources. Given how many variables there are, I hesitate to use my own experience as an example, but I'm happy to give back and pass on any knowledge I can.
I'm actually not a fan of CS50. I never took the course, but I went through the online material did the first few weeks of assignments. It is very broad and very shallow. It is also very hard and discouraging without some guided assistance. The students who take it for credit get a lot of help.
For a first CompSci course, the edX Python course is better, IMO.
There is a good, free book on Python that teaches practical skills for automating tasks. I sometimes recommend it to people, because it's immediately practical.
After that, you could try Flask or Django (Python web frameworks) and gradually introduce HTML, CSS, and JS.
There are also a couple of online courses that might be useful. I've only watched part of the first one -- it was good.
Nope, I'm sorry, he just told me that I should get into Python. He did recommend this edX course, but it's an introduction to Python, nothing to do with economics.
What do you want to learn? Programming or CS? CS is more than just programming, and CS theory is more than just Algorithms & Data Structures.
If you want to learn about Algorithms and Data Structures and you have a strong math background, then CLRS is the book to get: http://www.amazon.com/Introduction-Algorithms-Thomas-H-Corme...
An undergraduate CS curriculum will mostly cover the parts I-VI of the book (that's around 768 pages) plus a few chapters from the "Selected Topics Chapter" (we covered Linear Programming and String Matching). Mind you, this book is very theoretical, and all algorithms are given in pseudocode, so if you don't know any programming language, you might have to go with a an algorithms textbook that is more practical. In my DS course we had to implement a Red-Black tree and a binomial heap in Java, and in my Algorithms course we only wrote pseudocode.
Maybe Sedgewick's (Knuth was his PhD advisor!) "Algorithms (4th ed)" will be a better choice for a beginner, as it shows you algorithm implementations in Java: http://www.amazon.com/Algorithms-4th-Edition-Robert-Sedgewic... (If you decide to go this route, you might as well take his two Algorithms courses on Coursera, they will really help).
There are also a bunch of Python-based introductions to computer science which have a broader focus than just teaching specific data structures and algorithms. Some of them emphasize proper program design, debugging and problem solving. I haven't read any of them, so I can't vouch for them, but here are a few of the more popular ones:
This book was written to go along with John's edX course: https://www.edx.org/course/mitx/mitx-6-00-1x-introduction-co...
Oh and btw, there's also the Theory of Computation, which is a major part of CS theory. Here are a few MOOCs and recommended books on the subject:
Sipser's book is probably the best introduction to the theory of computation, and I believe its last chapter deals with Complexity theory as well.
I loved this book very much. It has a very informal and conversational style (don't let it fool you, the problem sets can be HARD).
Once you are familiar with some computation models, its time to study computational complexity and this is one of the best books on the subjects. It is used both for graduate and undergraduate courses.