DATA MANAGEMENT FOR DATA SCIENCE
An introduction to the principles and practices of managing and exploring different kinds of data. Work with both relational databases and Not only SQL (NoSQL) databases to organize and analyze structured, semi-structured, and unstructured data. Write basic queries, forecast time-series data, and evaluate data quality and patterns through exploratory data analysis. Explore data cleaning techniques such as interpolation and imputation, to address missing or inconsistent values. Apply data engineering techniques, including data pipelines, data warehouses, and processes for moving and transforming data using Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT). Learn how modern language models can be adapted using contextual information from custom datasets and strengthened with techniques that incorporate external knowledge sources.
Prerequisites & course information
Requisites: COMP SCI 320 , 400 , or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Deploy Structured Query Language (SQL) and Not Only SQL (NoSQL) databases to manage structured, semi-structured, and unstructured data and write programs to analyze the datasets. Audience: Undergraduate 2. Gain proficiency in data integration techniques such as Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT), and demonstrate competencies with each stage of the ETL or ELT process. Audience: Undergraduate 3. Investigate methods for analyzing and forecasting time-dependent data, evaluating the effectiveness of different modeling approaches for capturing temporal trends and seasonality. Audience: Undergraduate 4. Develop skills for adapting data-driven models using unstructured data to improve task-specific performance. Audience: Undergraduate 5. Design and implement information retrieval-based pipelines that integrate external knowledge sources to support context-aware analysis and decision-making. Audience: Undergraduate Learning Outcomes: 1. Deploy Structured Query Language (SQL) and Not Only SQL (NoSQL) databases to manage structured, semi-structured, and unstructured data and write programs to analyze the datasets. Audience: Undergraduate 2. Gain proficiency in data integration techniques such as Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT), and demonstrate competencies with each stage of the ETL or ELT process. Audience: Undergraduate 3. Investigate methods for analyzing and forecasting time-dependent data, evaluating the effectiveness of different modeling approaches for capturing temporal trends and seasonality. Audience: Undergraduate 4. Develop skills for adapting data-driven models using unstructured data to improve task-specific performance. Audience: Undergraduate 5. Design and implement information retrieval-based pipelines that integrate external knowledge sources to support context-aware analysis and decision-making. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027