4.8Course Overview
The Data Quality – The Complete Course certification provides comprehensive knowledge of 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 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.
Learning Options
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
Data Quality for Quantitative Survey Data
Data Quality for Quantitative Survey Data
Data Quality for Quantitative Survey Data
Data Quality for Quantitative Survey Data
Course Content
- What is Data Analytics?
- Solving business problems using data analytics
- Making business-defining decisions using data analytics
- Why do you need a data and analytics framework?
- The 4 aspects of the data and analytics framework
- Make better and faster decisions with data and analytics
- Data and analytics at PwC
- Data and analytics framework: tools and techniques
- The marketplace and emerging trends in big data analytics
- Business impacts of technology advancements and data trends
- What is Big Data?
- PwC's perspective on big data
- Data and analytics examples at PwC
- Identifying, organizing and processing data
- Exercises
- Types of data analysis techniques
- The role of Excel
- The role of SAS
- The role of R
- The role of Python
- The Power of Visualization
- The role of QlikView
- Exercises
- Data analysis approaches and techniques
- A Business Example of Data Visualization Tools
- Intro to Course Project
- Learn more about PwC
- Exercises
- Review course objectives and suggestions
FAQs
Data quality refers to the accuracy, completeness, consistency, and reliability of data. In Dubai, ensuring high data quality in quantitative survey data is crucial for making informed decisions, policy formulation, and maintaining credibility in research.
Data quality can be assessed through various measures such as completeness of responses, consistency of data across different sources, accuracy in data entry, and adherence to predefined standards and methodologies.
Common challenges include respondent bias, incomplete or inaccurate responses, language barriers, technological issues during data collection, and errors in data entry or transcription.
Strategies may include rigorous training of surveyors, employing advanced survey techniques, utilizing technology for real-time data validation, conducting pilot surveys to identify and address potential issues, and implementing quality control measures throughout the data collection process.
Data confidentiality and security can be ensured through strict adherence to data protection laws and regulations, secure data storage systems, encryption methods, limited access to sensitive information, and anonymization techniques.
Duration
Language
Batch Options
Upcoming Batch
Soft copy of Courseware
Certificate of Course Complition
Experienced and Professional Trainer
Lab and Exercises During Training 
Sample Questions for Exam Prepration
Mock Test on the Last Day of Training 
Exam Registration Guidance 
5-Star Training Environmment 