Spring 2027

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.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 2

Lectures are large, with a median of 144 enrolled students in the recorded sections.

Section enrollment · Fall 2026 snapshot ↗

What you’ll be able to do

  • 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.

  • 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.

  • Investigate methods for analyzing and forecasting time-dependent data, evaluating the effectiveness of different modeling approaches for capturing temporal trends and seasonality.

  • Develop skills for adapting data-driven models using unstructured data to improve task-specific performance.

  • Design and implement information retrieval-based pipelines that integrate external knowledge sources to support context-aware analysis and decision-making.

Source: UW Guide

Grade history

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average GPA
letter grades

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Prerequisites

Course map

COMP SCI 320, 400, or graduate/professional standing

COMPSCI 574

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Professors

Spring 2027

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Calendar & sections

Spring 2027

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Student experience

the class

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.

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Sources & history

UW–Madison

Catalog & offerings

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Selected offering source records
COMPSCI 574 · Spring 2027

Data Management for Data Science

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:266:027413",
    "course_id": "COMPSCI 574",
    "course_uid": "course_e2127d774d1519c358b75a95",
    "term_id": "1274",
    "source_course_id": "027413",
    "source_subject_id": "266",
    "title": "Data Management for Data Science",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "85afab9875b930cc2654c12dfdc9868a54d72670ad165ed8a7352a99e505d342",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}