Algorithms and data structures constitute the fundamentals of programming.
Good understanding of algorithms and data structures is one of the most important requirements for a great number of work positions. You'll have to solve many problems related to algorithms and data structures at coding interviews. Indeed, you can live without an understanding of algorithms and data structures, in general. However, you can do that until you face a real problem which requires to choose right data structures and implement custom algorithms. If you haven't ever faced such problems, believe me, this is just a matter of time. One day, you'll face such a problem and if you don't understand algorithms and data structures in depth, you'll feel yourself ashamed and helpless. You'll be stuck.
If you're not good at algorithms and data structures, you'll never pass a coding interview in a decent company. Almost all the companies prefer to hire those developers who have good understanding of algorithms and data structures, remember that. Do not delay the study of fundamental concepts.
Better hardware is not a solution for all the performance-related problems. Sometimes, you need to write software for very slow devices. And that very often depends on the budget of a customer, by the way, so you don't have a control over it.
You'd better to understand what's going on under the hood at least one level in-depth. Indeed, if you don't understand how List works, sometimes you'll make sub-optimal or completely wrong decisions.
Introduction to Algorithms and Data Structures: what is a data structure, abstract data type and what's the difference between these notions. What is an algorithm and why they are important to us?
Introduction to Algorithm Analysis: determine how long will a program work, build a log-log plot, approximations, order of growth (Big-O notation), memory consumption
Arrays: arrays in C#, arrays in memory, time complexity of operations on arrays
Sort Algorithms: bubble sort, selection sort, insertion sort, recursion, shell sort, merge sort, quick sort, stability of sort algorithms
Lists: List built-in .NETBCL, nodes, linked list including singly and doubly linked lists, linked list built-in .NET
Stacks: theory and practice; stack based on array, stack based on a linked list, stack built-in .NET
Queues: theory and practice; queue based on array, circular queue, queue based on linked list, queue built-in .NET BCL
Search Algorithms: linear search, binary search (more will be added in the future)
Symbol Tables: intro, API, sequential search-based, binary search-based
Hash Tables: intro, hash functions, GetHashCode, approaches to collisions resolving: separate chaining and linear probing, Dictionary built-in BCL, Sets in BCL
Trees: binary search tree (BST), implementing BST (more will be added in the future)
Heaps: intro, heaps and arrays, implementation, Heap Sort (more will be added in the future, specifically about Priority Queues)
Immutable Collections: immutability and memory pressure, immutable stacks and queues, immutable list, immutable sets, immutable dictionaries, builders in immutable collections
Different Algorithms. This section will include different algorithms as you might guess. For now it convers the Sieve of Eratosthenes.
