University of Michigan - Design Strategies for Maximizing Total Data Quality
- Offered byCoursera
Design Strategies for Maximizing Total Data Quality at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Design Strategies for Maximizing Total Data Quality at Coursera Highlights
- Earn a Certificate upon completion University of Michigan
Design Strategies for Maximizing Total Data Quality at Coursera Course details
- Learn about design tools and techniques for maximizing TDQ across all stages of the TDQ framework during a data collection or a data gathering process
- Identify aspects of the data generating or data gathering process that impact TDQ and be able to assess whether and how such aspects can be measured
- Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data
- Develop solutions to hypothetical design problems arising during the process of data collection or data gathering and processing
Design Strategies for Maximizing Total Data Quality at Coursera Curriculum
Introduction and Maximizing Validity and Data Origin Quality
Welcome to Course 3 and the final course in the Specialization!
Maximizing Validity for Designed Data
Case Study: Improving Questions Based on Pre-Testing Results
Maximizing Validity for Gathered Data
Case Study: Improving the Validity of Gathered Data using Auxiliary Data and Transformations
Maximizing Data Origin Quality for Designed Data
Case Study: Standardized vs. Conversational Interviewing
Maximizing Data Original Quality for Gathered Data
Case Study: Simple Lessons Learned for Improving Data Origin Quality While Web Scraping
Course Syllabus
Course Pre-Survey
Case Study pre-read: Improving Google Flu Trends Estimates for the United States through Transformation
Optional: links from previous lecture on Maximizing Data Original Quality for Gathered Data
Design Strategies for Maximizing Validity
Design Strategies for Maximizing Data Origin Quality
Maximizing Processing and Data Access Quality
Maximizing Processing Quality for Designed Data
Example: Double Data Entry and Imputation to Maximize Data Processing Quality
Maximizing Processing Quality for Gathered Data
Example: Maximizing Processing Quality for Gathered Data
Maximizing Data Access Quality for Designed Data
Maximizing Data Access Quality for Gathered Data
Example: Maximizing Data Access Quality for Gathered Data
Exploring and Evaluating Enhancements for ABS Sampling Frames
Design Strategies for Maximizing Processing Quality
Strategies for Maximizing Access Quality
Maximizing Data Source Quality and Minimizing Data Missingness
Maximizing Data Source Quality for Designed Data
Example: Maximizing Data Source Quality for Designed Data
Maximizing Data Source Quality for Gathered Data
Minimizing Data Missingness for Designed Data
Example: Imputation and Weighting Adjustment
Minimizing Data Missingness for Designed Data: Responsive and Adaptive Survey Design
Minimizing Data Missingness for Gathered Data
Example: Minimizing Data Missingness for Gathered Data
Probability Samples of Twitter
Optional: .csv and .py files for the next lecture
Strategies for Maximizing Source Quality
Strategies for Minimizing Data Missingness
Maximizing the Quality of Data Analysis
Maximizing the Quality of an Analysis of Designed Data
Case Studies in Analytic Error
Maximizing the Quality of an Analysis of Gathered Data
Case Study: Maximizing the Quality of an Analysis of Video Image Data
Course and Specialization Conclusion
References for Design Strategies for Maximizing Total Data Quality
Course and Specialization Post-Survey
Maximizing Data Analysis Quality
Design Strategies for Maximizing Total Data Quality at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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