Thank you for your reply. I do intend to work further on my skills in order to get better!
For example, you could take python or java (or C#, pretty much any general OOP language), and make a simple crud application. Then add a "reporting" page where you can see different results on for example uses of trees, sorting algorithms or storage data structures (like linked lists, hash maps etc). The crud part can be your familiar part where you have a good feel of what's what, and the report/test/whatever page can be your sandbox to see how your experiments deal with your data.
One of the books I found helpful was Data Structures and Problem Solving Using Java by Mark A. Weiss.
A softer start can be found with http://interactivepython.org/runestone/static/pythonds/index...
If you want something using languages you already know, that's fine too. Say for example you wanted to simply know how to build a tree, or more generally a graph. Those concepts may sound big or complex, but they are only as big or as complex as you want them to be allowing you to build a simple version (i.e. a class that is a node in a linked list, and it holds just a number as a value and the next node for your list as a pointer or value or whatever you like). It's something you could do in 10 lines of code just to get yourself started. Building on top of that is a whole lot easier than jumping straight into AVL trees and A* path finding algorithms.
Starting light, say, making a few things yourself in a language of your choice will get you a good feel as to what you want to explore and how easy it is to grasp the concepts, for example:
- linked lists and doubly linked lists
- binary trees (binary doesn't mean binary data, don't worry)
- sorting (check quicksort, shellsort, bubblesort on wikipedia for starters to get an animation for an idea what it is)
- heap
- stack
- (priority-) queue
If you don't know his name, you should - he's written some of the best books on these algorithms and his MOOC is similarly rich in visualizations and concrete examples that help you develop the intuition for the algorithms. Also incidentally his doctoral advisor was Donald Knuth, who he evidently learned a lot from.
