CEIS295 / COMPLETED ACADEMIC PROJECT
Data Structures & Algorithm Performance
Choosing a structure for the workload.
A Python coursework project comparing data structures and sorting approaches across different record-processing tasks.
DeVry University coursework01 / THE OBJECTIVE
Data Structures & Algorithm Performance
The same collection of records can behave very differently depending on how it is stored, searched, updated, and sorted. The project explored those tradeoffs through practical timing exercises.
02 / THE WORK
My contribution.
- Worked with client-record objects and CSV input to examine ArrayList, LinkedList, and BinarySearchTree operations.
- Compared adding, retrieving, and removing records in queue, customer-service, and call-center scenarios.
- Examined Bubble, Selection, Insertion, Shell, Quick, and Merge sorting approaches across datasets from 100 to 100,000 records.
- Documented code, execution output, comparisons, and a queue-based call-distribution simulation.
ArrayList · LinkedList · Search tree
Add · Retrieve · Remove · Sort
Timing · Access pattern · Tradeoffs
03 / PROJECT EVIDENCE
What the work produced.
A completed April 2024 presentation containing Python examples, execution screenshots, timing tables, and an analysis of workload-dependent choices.
Evidence reviewed: CEIS295 project presentation, April 18, 2024. Source pages 4–7, 9–15, 17–24.
04 / WHAT I LEARNED
The reasoning behind the result.
A useful performance comparison starts with the operation an application needs to do. A fast result for one access pattern does not make a data structure the best choice for every workload.
05 / WHAT THIS DEMONSTRATES
Practical skills in context.
Implementing data-processing logic and explaining tradeoffs.
Project deliverable: Object models, CSV processing, queue simulation, and performance comparisons.
Status & scope
Completed academic analysis. Timing results document the original coursework and have not been rerun or independently benchmarked.
Based on the original project presentation, reviewed October 17, 2026. Historical project evidence; not a new execution test.