CS 469 Data Structures and Algorithms in Computing *

This course amalgamates data science and software engineering in a pragmatic manner. This course shows students how to derive value from data for an organization. The course covers project management and communications, as well as methods for data understanding and preparation that professional data scientists use to create and maintain a problem-solving pipeline. The course introduces students to the most useful data science frameworks and tools. This course explains, using many examples, all phases of a data science life cycle model from project initiation to data exploration and retrospection. Specific course topics include problem formulation; data acquisition, cleaning, and integration; feature engineering and extraction; data visualization; project management; teamwork and collaboration; and technical writing and presentations. 

Credits

5

Prerequisite

For students to succeed in this course, CS 351 and IS 201 are required pre-requisites.

Outcomes

1. This course will prepare students to:
2. Understand basic data structures in programming.
3. Understand simple numerical algorithms (e.g., computing the average, finding the min, etc.).
4. Apply sorting and searching algorithms.
5. Analyze the shortest path in a graph or tree using an efficient algorithm, such as a greedy algorithm.
6. Evaluate a graph or tree traversal using the general framework of a breadth or depth first algorithm.
7. Create dynamic programming solutions for appropriate problems.