Introduction to Data quality for quantitative survey data.

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Course Overview

The Data Quality – The Complete Course certification provides comprehensive knowledge on data quality concepts, principles, and techniques to maintain high-quality data. It emphasizes methodologies for continuous quality improvement and data governance strategies. The certification is vital to various industries such as health, finance, and technology, where reliability and accuracy of data are critical. It aids in ensuring data integrity, reducing errors, and improving decision-making processes. It also covers data profiling and cleansing, managing data quality in data integration, and using data quality tools. This certification helps in understanding, measuring, and improving data quality, thereby boosting operational efficiency.

Learning Objectives

The learning objectives of the Data Quality – The Complete Course would aim to equip learners with comprehensive knowledge about the concept, importance, and processes of ensuring data quality. Students would learn to identify and rectify common data issues, improve data quality by using proven strategies and tools, understand the regulations related to data quality, and interpret data quality metrics. They would also learn to design a data quality framework and implement data quality management in their organization. The course would provide them with the skills to become a key player in making data-driven decisions and ensuring the trustworthiness and efficacy of the data used.

Course Prerequisites

This course is intended for individuals with at least a basic understanding of data concepts. Prior knowledge will be helpful, but not required. Some topics that will be covered include data cleansing and validation, data standards and conventions, data validation and consistency, and data integrity. You must possess a basic understanding of the different types of data, including relational data, structured data, and big data. Familiarity with data modeling, databases, and SQL will also be helpful. Additionally, some knowledge of programming may be beneficial, as the course will involve the use of Python.

Target Audiance

  • • Data professionals involved in data management and reporting.
  • • Business analysts or consultants aiming to improve their understanding of data quality aspects.
  • • IT managers or project leaders dealing with dataintegration or migration projects.
  • • University students interested in datascience or information systems fields.
  • • Professionals aiming to gain proficiency in data quality improvement strategies.

Schedule Dates

Introduction to Data quality for quantitative survey data.
20 May 2024 - 24 May 2024
Introduction to Data quality for quantitative survey data.
26 August 2024 - 30 August 2024
Introduction to Data quality for quantitative survey data.
02 December 2024 - 06 December 2024
Introduction to Data quality for quantitative survey data.
03 March 2025 - 07 March 2025

Course Content

  • What Are Designed Data?
  • Example: Developing an Online Survey with SurveyMonkey
  • What are Gathered Data?
  • Example: Scraping Data from the Web
  • Hybrid Data: Designed and Gathered
  • The Total Data Quality Framework
  • Interview: Perspectives on the Meaning of Total Data Quality

  • Defining Validity
  • Threats to Validity for Designed Data
  • Cognitive Interviewing (Think Aloud)
  • Try It Out: Using the Survey Quality Predictor Application
  • Threats to Validity for Gathered Data
  • Defining Data Origin
  • Data Origin Threats for Designed Data
  • Data Origin Threats for Gathered Data
  • Defining Data Processing
  • Data Processing Threats for Designed Data
  • Data Processing Threats for Gathered Data
  • Case Study: Author Name Ambiguity in Bibliographic Data
  • Exercises

  • Defining Data Access
  • Defining Target Populations
  • Data Access Threats for Gathered Data
  • Case Study: Random Samples from Twitter APIs May Not Be Random
  • Data Access Threats for Designed Data
  • Case Study: Evaluating Sampling Frames/Commercial Data
  • Data Source Definition
  • Data Source Threats for Designed Data
  • Data Source Threats for Gathered Data
  • Case Study: How Content and User Characteristics Can Impact Quality of Gathered Data
  • Data Missingness Threats for Designed Data
  • Imputing Missing Values Demo, Before and After Estimates
  • Exercise

  • Why is Data Analysis Part of Total Data Quality?
  • Threats to the Quality of Data Analysis for Designed Data
  • Demo: Alternative Approaches to Analyzing Survey Data
  • Threats Concerning Data Analysis for Gathered Data
  • Case Study: Algorithm Bias in Gathered Data
  • Case Study: Analytic Error in NCSES Surveys
  • Optional Tutorial: Using the Free R Software
  • Exercise


Data quality refers to the accuracy, completeness, consistency, and reliability of data collected through quantitative surveys in Dubai. It ensures that the data accurately represents the phenomenon being studied and can be relied upon for analysis and decision-making.

Data quality is crucial for ensuring the validity and reliability of survey findings. In Dubai, where decisions often rely on data-driven insights, high-quality data ensures that policies, strategies, and initiatives are based on accurate information, leading to more effective outcomes.

Data quality can be assessed through various measures, including completeness of responses, consistency of data across different variables, absence of errors or outliers, and adherence to predefined quality standards. Additionally, comparing survey results with known benchmarks or conducting validation checks can help evaluate data quality.

Common challenges to data quality in Dubai include language barriers, cultural sensitivities impacting survey responses, sample bias due to demographic characteristics, respondent fatigue leading to incomplete or inaccurate responses, and technological issues during data collection or entry.

Improving data quality involves careful planning and execution at every stage of the survey process. This includes designing clear and concise survey instruments, training data collectors effectively, ensuring adequate sample representation, implementing data validation checks, and utilizing appropriate technologies for data capture and management.

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