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CS 225 - Data Structures

Spring 2021

Official Description

Data abstractions: elementary data structures (lists, stacks, queues, and trees) and their implementation using an object-oriented programming language. Solutions to a variety of computational problems such as search on graphs and trees. Elementary analysis of algorithms. Course Information: Prerequisite: CS 125 or ECE 220; One of CS 173, MATH 213, MATH 347, MATH 412 or MATH 413. Class Schedule Information: Students must register for one lecture-discussion and one lecture section.

Related Faculty

Learning Goals

Implement classic and adapted data structures and algorithms (1), (2), (6)
Navigate, organize, compile C++ projects of moderate complexity (many objects and dependencies) in Linux. (1), (2), (6)
Use basic editing and debugging tools such as GDB and Valgrind. (1), (2), (6)
Analyze the efficiency of implementation choices. (1), (2), (6)
Decompose a problem into its supporting data structures such as lists, stacks, queues, trees, etc. (1), (2), (6)
Diagnose appropriate approaches or algorithms to solve problems involving graph search, tree traversal, optimization, data organization, etc. (1), (2) , (6)

Topic List

C++ programming (compilation, classes, pointers, parameters, dynamic memory, memory management, inheritance, templates, generic programming)
Data structures - ADTS - (lists, stacks, queues, trees, dictionaries, priority queues, disjoint sets, graphs).
Data structures - implementation (linked memory, BST/AVL, B-tree, hash table, kd-tree, quad-tree, heap, union-find (up-trees), adjaceny list / arrays).
Algorithms (tree traversal, nearest neighbor, buildHeap, heapsort, BFS, DFS, MST, shortest paths)

Assessment and Revisions

Revisions in last 6 years Approximately when revision was done Reason for revision Data or documentation available?
Incremental changes to MPs 3/4 sp11 increase connections to surrounding curriculum (i.e. sorting, graph traversal) Informal discussion with CS125 Instructor (Angrave)
Lab exercises redesigned to be a) coupled tightly with lecture content, and b) tested and graded for credit. sp10 Success of labs depended on varied teaching skills of course staff, and they were optional, and thus poorly attended. Student feedback, ICES 08-10.
Grading policy changed to include credit for early submission of MPs, and also to provide nightly grading feedback. sp09 Motivate students to organize the assignment window more carefully. Informal discussion with CS225 course staff.
Addition of Parallel Lab Exercises (6 instructional hours) sp11 Parallel computing is pervasive. NSF/IEEE-TCPP Curriculum Initiative
Developed CoMoTo - the collaboration monitoring toolkit. sp09 ongoing Observed increase in plagiarism. We wanted to increase the advising and counseling component of instruction while also maintaining the integrity of the course. GATE08
Increased discussion of applications of data structures. sp09 Broaden applicability of newly learned skills. Informal discussion with course owner Chekuri
Deployed a new educational software testing framework. fa11 Reduce the burden for understanding our given test code, allowing students to focus on the actual test cases. Informal discussion with CS225 course staff.

Required, Elective, or Selected Elective


Data StructuresAL131208OLC41100 - 1150 M W F    Carl Evans
Brad R Solomon
Data StructuresALP59777OLC41100 - 1150 M W F    Carl Evans
Brad R Solomon
Data StructuresAYB31218OLB01200 - 1350 W    
Data StructuresAYC31222OLB01400 - 1550 W    
Data StructuresAYD31225OLB01600 - 1750 W    
Data StructuresAYE31227OLB01800 - 1950 W    
Data StructuresAYF31229OLB02000 - 2150 W    
Data StructuresAYG31231OLB00900 - 1050 F