Course Description
Students are exposed to current approaches, techniques and best practices for collecting, cleaning and normalizing data, processing, storing, managing, securing and preparing structured and unstructured big data sets for analytics.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Identify and differentiate among structured, semi-structured, and unstructured data, especially within informatics contexts.
- Understand relational database concepts and apply normalization principles.
- Write SQL queries for data exploration, transformation, and cleaning.
- Design reusable and modular SQL logic using views and common table expressions (CTEs).
- Execute scalable data preprocessing using Spark SQL on large datasets.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Weekly Labs and Assignments: SQL and data-preparation exercises | 35% |
| Midterm Mini Project: Applied SQL project | 20% |
| Final Project: End-to-end data-preparation project | 35% |
| Quizzes and Participation: Concept checks and course engagement | 10% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
No course materials are required.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.