DS 220Data Management for Data Sciences
The course introduces students to the fundamentals of data models: organizing, managing, and using different types of data that arise in real-world applications. The course introduces students to several alternative data models and database solutions, emphasizing their strengths and limitations in the context of real-world applications. Topics covered include the relational databases, key-value stores, column-oriented databases, vector-space databases, graph databases, and distributed file systems together with their applications in solving real-world big data management problems. Upon completion of the course, the students will be able to choose an appropriate data model and database solution for a given application, and use the chosen database to organize, manage, and use data in the context of specific applications.
Prerequisites
From the catalog: “C or better in CMPSC 121 or CMPSC 131 Enforced Concurrent at Enrollment: CMPSC 122 or CMPSC 132”
Where it sits in degree plans
Suggested plans most often put it in Year 2 · Fall (4 programs).
Required in (5)
- Data Sciences, B.S. (Abington)
- Data Sciences, B.S. (Engineering)
- Data Sciences, B.S. (Information Sciences and Technology)
- Data Sciences, B.S. (Science)
- Social Data Analytics, B.S.
Courses that list DS 220 as a prerequisite (10)
- CMPSC 410 Programming Models for Big Data
- CMPSC 446 Data Mining
- DS 300 Privacy and Security for Data Sciences
- DS 320 Data Integration
- DS 402 Emerging Trends in the Data Sciences
- DS 410 Programming Models for Big Data
- DS 420 Network Analytics
- DS 435 Ethical Issues in Data Science Practice
- DS 440 Data Sciences Capstone Course
- DS 441 Information Retrieval and Organization