Coursera - Algorithms Specialization by Stanford University

Learn To Think Like A Computer Scientist. Master the fundamentals of the design and analysis of algorithms.

Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

Graph Search, Shortest Paths, and Data Structures
The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).