TOPICS

previous material:
big-Oh, algorithm analysis
using lists, stacks, queues, priority queues, hashtables (sets or maps)

binary trees: 
recursive methods, pre/in/post-order traversals, predecessor/successor

binary search trees: 
definition, find, insert, remove

AVL trees: balance factors, rotations, insertion w/ balancing

sorting:
pros/cons (space/time, best/average/worst, etc.) for insertion sort, 
merge sort, quick sort, bucket sort
choice of pivots for quick sort, concept of partitioning 

graphs:
pros/cons of adjacency matrix, adjacency list
DFS/BFS. stack vs. queue, keeping track of paths, avoiding already visited
other search extensions, e.g. topological sort, strong connectivity
how to formulate a problem into a graph
dijkstra's algorithm.  what it does, algorithm analysis of.  
minimum spanning tree.  prim's, kruskal's.  
harder graph problems

supplemental topics:
concepts behind b-trees
data structures & algorithms and their role in internet-scale computing

SOME SPECIFIC PROBLEMS TO EXPECT

given a real-world problem, what data structures or algorithms to use and 
why?

given two data structures or algorithms, what are the pros/cons of using 
one over the other?

show the result after performing an insertion (possibly involving 
rotations) on an AVL tree

given an undirected graph, show the order of vertices added through 
Prim's algorithm.  show the order of edges added through Kruskal's 
algorithm.  (basically simulating the algorithms)

show how a problem can be phrased as a graph and can be solved with 
modifications of graph algorithms

coding problem involving binary search trees

coding problem involving graphs

a question or two reinforcing concepts addressed on hws/prjs