DATA EXPLORATION, CLEANING, AND INTEGRATION FOR DATA SCIENCE
Big Data is often said to deal with four Vs: volume, velocity, variety, and veracity. The focus is on variety and veracity challenges, which often arise in data science projects. In many such projects, data is often incorrect, hard to understand, and come from a variety of sources. Data scientists often spend 80% of their effort to explore, clean, and integrate this data, before analysis can be carried out to extract insights. As a result, managing variety and veracity has received significant attention. Study these topics, understand their challenges, and discuss solutions. These solutions often require data management, machine learning, big data scaling, cloud, crowdsourcing, and user interaction techniques. Knowledge of machine learning/AI [ COMP SCI 540 ], databases [ COMP SCI 564 ] and Python [ COMP SCI 320 ] recommended.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Identify and examine the key challenges of managing variety and veracity with large data sets. These include data acquisition, data extraction, data exploration, cleaning, matching, and merging data. Audience: Graduate 2. Summarize the variety and veracity solution approaches in academic and industry. Audience: Graduate 3. Design and apply course concepts to experiential learning through a research project. Audience: Graduate 4. Effectively communicate through written reports, oral presentations, and discussions. Audience: Graduate Learning Outcomes: 1. Identify and examine the key challenges of managing variety and veracity with large data sets. These include data acquisition, data extraction, data exploration, cleaning, matching, and merging data. Audience: Graduate 2. Summarize the variety and veracity solution approaches in academic and industry. Audience: Graduate 3. Design and apply course concepts to experiential learning through a research project. Audience: Graduate 4. Effectively communicate through written reports, oral presentations, and discussions. Audience: Graduate
Sources
- Course catalogue ↗2026-2027