Course materials
Spring 2025 · Spring 2026
01 · Syllabus
This course explores structures for storing and organizing data and the algorithms used to manipulate them. Students connect theoretical foundations with practical implementations, analyze efficiency, compare iterative and recursive approaches, and study searching, sorting, and traversal. Topics include lists, stacks, queues, trees, hash tables, graphs, and related abstract data types.
PyCharm is the primary programming environment and is available free to students.
Weekly assignments require working code and a clear report explaining the solution process, ideas, errors, debugging decisions, and corrections. Both code and report must be submitted; late submissions are not accepted.
Assignments constitute 40%, the final project 15%, and exams 45% of the course grade. The published letter scale begins with A at 93, A− at 90, B+ at 87, and continues through the syllabus thresholds.
The classroom must remain respectful and free of discrimination, bullying, and other harmful conduct. Violations are addressed through course and college procedures.
Midterm: Thursday, March 5, in class. Final: Tuesday, May 5, beginning at 9:00 AM.
AI can be a useful assistant when used reasonably, but it must not replace a student’s thinking. Assignments must be completed independently. Students must understand, reproduce, and explain submitted work; significant inconsistencies may require an in-person demonstration and may be reported under academic-integrity procedures.
02 · Contents
Running time, basic-operation counting, mathematical notation, asymptotic dominance, Big-O, Omega, Theta, and worst-, average-, and amortized-case analysis.
Abstract data types, dynamic arrays, binary search, selection sort, insertion sort, merge sort, quicksort, in-place algorithms, and performance comparisons.
The stack ADT, Python implementations, public and private attributes, preconditions, recursive merge sort, balanced parentheses, and next-greater-element problems.
List- and stack-based queues, round-robin scheduling, priority queues, and recursive queue operations.
Node and linked-list classes, traversal, indexed access, mutation, cycle detection, and complexity comparisons with array-backed lists.
Recursive tree definitions, visualization, traversal orders, mutation, and the relationship between recursive structures and recursive algorithms.
Search, inorder traversal, insertion, deletion, correctness, and efficiency under balanced and unbalanced shapes.
Max-heap structure, list representation, build-heap, bubble-up and bubble-down, extraction, insertion, equal priorities, and heap sort.
Graph representations, recursive connectivity, cycle detection, paths, trees, spanning trees, and visited-set reasoning.
Hash functions, closed and open addressing, collision handling, linked-list buckets, probing, and implementation tradeoffs.
Node design, insertion, exact and prefix search, deletion, running time, and comparisons with hash tables.
Balanced-search-tree invariants, rotations, insertion repair, deletion repair, and logarithmic operation guarantees.
03 · Exam preparation
The student is expected to conduct himself, at all times, both on and off the campus, as a gentleman and a responsible citizen.
I will complete this exam in a fair, honest, respectful, responsible, and trustworthy manner. This means that I will complete the exam as if the professor was watching my every action. I will act according to the professor’s instructions, and I will neither give nor receive any aid or assistance other than what is authorized. I know that the integrity of this exam and this class is up to me, and I pledge not to take any action that would break the trust of my classmates or professor, or undermine the fairness of this class.
04 · Assignments
Open each assignment to work directly from the original questions and code prompts.
05 · Projects