Data Quality Management is an essential aspect of any organization that deals with vast amounts of data. It involves ensuring that data is accurate, complete, and consistent, thereby improving decision-making and overall business performance. As the demand for professionals with expertise in this field continues to rise, many individuals seek to gain knowledge and skills through online courses. This article provides an overview of the best Data Quality Management courses available online, outlining their features, benefits, and potential career opportunities for learners.
Here’s a look at the Best Data Quality Management Courses and Certifications Online and what they have to offer for you!
10 Best Data Quality Management Courses and Certifications Online
- 10 Best Data Quality Management Courses and Certifications Online
- 1. Data Warehouse ETL Testing & Data Quality Management A-Z by Lorenz DS (Udemy) (Our Best Pick)
- 2. 4×1 Data Management/Governance/Security/Ethics Masterclass by Vasco Patrício (Udemy)
- 3. SQL Server Master Data Services for Master Data Management by Siddharth Mehta (Udemy)
- 4. Data Quality Fundamentals by Sid Inf (Udemy)
- 5. Data Quality Masterclass – The Complete Course by George Smarts (Udemy)
- 6. Informatica Data Quality Analyst – Beginner’s Guide by Sid Inf (Udemy)
- 7. The Complete Data Quality and Digital Transformation Course by Bing Yu (Udemy)
- 8. Improving data quality in data analytics & machine learning by Mike X Cohen (Udemy)
- 9. Data Quality & Profiling with ETL Pentaho DI & DataCleaner by Rajkumar V (Udemy)
- 10. DQS Training by Vikas Munjal (Udemy)
This course, titled “Data Warehouse ETL Testing & Data Quality Management A-Z,” aims to provide beginners with practical exercises and a certificate of completion. It teaches the essentials of ETL (Extract, Transform, Load) Data Warehouse Testing and Data Quality Management, including frequently used queries, reporting, and monitoring. The course also covers how to build database views for Data Quality monitoring and visualization, as well as some common mistakes made during ETL/ELT (Extract, Load, Transform) tests.
The course materials include an Excel file for practice and a short quiz at the end of each module, with a final quiz at the end of the course. The course also requires basic knowledge of SQL and optionally some experience with visualization tools. Basic setup of a database (PostgreSQL, Oracle) and visualization tool (Qliksense) is recommended.
The course consists of six modules, including an introduction to ETL/ELT Testing and Data Quality Management, building database views for Data Quality Monitoring, building dashboards for Reporting, exercises, and a final quiz. It is designed for students who want to learn the basics of ETL/ELT testing and Data Quality Management, as well as business analysts, data analysts, software engineers, and data stewards and managers who want to learn more about practical examples and apply data quality standards within their organization.
The course is concluded with a final note and a certificate of completion. Overall, this course provides a comprehensive introduction to ETL testing and data quality management with practical exercises and quizzes, suitable for beginners and professionals alike.
The 4×1 Data Management/Governance/Security/Ethics Masterclass is a comprehensive course designed to provide detailed information on data literacy, data quality operations, data governance policies, and data security/privacy controls. The course is targeted at individuals involved in data projects in their organizations and those interested in improving their data quality knowledge.
The course covers essential principles for successful data initiatives, the key data disciplines, and the main activities in data management and data governance. The course also delves deep into the stages of the information lifecycle, progresses from projects to processes, and offers an assessment of an organization’s sophistication levels in managing data.
The course offers an in-depth understanding of different types of data and the types of data quality issues and their financial impact. It also covers the usual DQ improvement process, the three main types of DQ actions, and the different data dimensions used when analyzing DQ problems. Additionally, it provides information on the effect of big data and AI in data management, the tools used for DQ management, and building a business case for DM/DG.
The course covers the common functions and capabilities enabled by DG, including privacy and security controls, lineage tracking, metadata management, data classification, and monitoring. It also covers data stewardship, roles and responsibilities in DG, and how to deploy and maintain a DG program.
The course offers an understanding of cryptographic protection, data retention and disposal, physical media protection, and service provider assessment and monitoring. It also provides information on geographical regulation affects data privacy, data governance structures, and defining security controls by data classification, media downgrading and/or redacting, data de-identification, and anonymization.
Participants are offered a 30-day money-back guarantee and free preview videos to ensure the course’s suitability. The course also includes additional modules on extra security controls and pitching technical projects and bonus lectures.
This course is titled “SQL Server Master Data Services for Master Data Management” and is instructed by Siddharth Mehta. The course covers topics such as MDM, SQL Server, Data Modeling, Data Integration, Data Quality, Data Management, Business Intelligence, and Data Architect. The course offers two main benefits for students; they can learn a niche skill from an experienced instructor, and they can add Master Data Services to their skill set, making them more attractive to recruiters.
The course is designed to provide comprehensive knowledge on Master Data Management concepts and implementation using Master Data Services. Students will have access to sample data models with practice exercises and demonstrations presented in the course. Anyone interested in specializing in MDM should consider pursuing this course as it is seen as a big advantage skill by Database Development/Database Administration/Business Intelligence Professionals.
The course covers various aspects of Master Data Management and Master Data Services, such as Master Data Management Concepts and Hub Architecture, Master Data Services Installation and Configuration, Concept Development, Implementation of all MDM constructs, Enterprise Integration of Master Data Services with external systems, Data Publishing and Deployment, Security, and Best Practices.
The course is divided into several sections, including Basics of Master Data Management, Installing and Configuring Master Data Services, Master Data Services – Terminologies and Concepts, Data Modeling and Data Integration using Master Data Services, Data Management with Master Data Services, and Summary.
The Data Quality Fundamentals course, led by instructor Sid Inf, aims to provide learners with a comprehensive understanding of data quality. The course emphasizes that data quality involves more than just fixing incorrect data, and is a continual process due to the changing nature of business processes, customer expectations, source systems, and business rules. To achieve high-quality data, companies must commit to data quality management principles and develop programs that reduce data defects over time.
The course covers various topics related to data quality, including its dimensions, differences from data governance, the data life cycle, data quality life cycle, data profiling, business impacts of low data quality, and data quality roles. Through this course, learners can gain a deeper understanding of data quality and its importance in overall data management.
Overall, the Data Quality Fundamentals course is designed to provide learners with a solid foundation in data quality principles, terminology, and concepts. By completing this course, learners can develop the necessary skills and knowledge to effectively manage data quality in their respective organizations.
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The Data Quality Masterclass – The Complete Course is a comprehensive guide to learning Data Quality Management from A to Z. The course is designed to teach the latest best practices from the Data Industry, and is structured in a way that is easy for beginners to get started. The course covers all Data Quality Principles, Techniques, and much more, providing a deep understanding of the Data Quality Management discipline.
The course covers a variety of topics, including what Data Quality is, what Data Quality Management is, why Data Quality is important, and how it affects your business. It also covers the different dimensions of Data Quality, Data Quality Rules, Profiling, Data Parsing, Data Standardization, Identity Resolution, Record Linkage, Data Cleansing, Data Enhancement, and much more. Students will learn about the Data Quality Process, the different Data Quality Roles, Data Quality Tools, and Data Quality Best Practices.
Enrolled students will enjoy lifetime access to the course and all future updates, 4.5 hours of high-quality, up-to-date video lectures, and a practical Data Quality course with step-by-step instructions on how to implement the different techniques. The course content is divided into several sections, including Introduction, The Basics, 6 Key Data Quality Dimensions, Data Quality Rules, Data Quality Techniques/Tools, Data Quality Roles, Data Quality Process, Data Quality Tools, Data Governance (optional), Data Quality Best Practices, and What Next.
Overall, the Data Quality Masterclass – The Complete Course is a thorough and informative course for anyone looking to learn about Data Quality Management. George Smarts is the course instructor and provides contextual examples to showcase why Data Quality is important and how to use Data Quality principles to manage the data in your organization.
The “Informatica Data Quality Analyst – Beginner’s Guide” course provides an introduction to Informatica Analyst or IDQ Analyst, a web-based application client that allows analysts to analyze, cleanse, standardize, profile, and score data in an enterprise. The course is instructed by Sid Inf and is suitable for beginners.
Informatica Analyst is used for data-driven collaboration between business analysts and developers. It offers features such as column and rule profiling, scorecarding, and bad record and duplicate record management. Reference data can also be managed and provided to developers in a data quality solution.
Organizations use Informatica Analyst to profile data, create rules in profiles, score data, manage reference data, and manage bad records and duplicate records. Creating and running a profile allows analysts to analyze the structure and content of enterprise data and identify strengths and weaknesses. Rules can be applied within profiles to validate data and measure data quality progress.
Scorecards can be created to score the valid values for any column or the output of rules. The value frequency for columns in a profile is displayed as scores, allowing for the measurement and visualization of data quality progress. Reference tables can also be created and updated for use by analysts and developers in data quality standardization and validation rules.
The course covers several sections, including an introduction to Informatica Analyst, data quality, analyst vs data quality, what is involved, mapping specification, and reference table. The course also includes a section on the limited features of Informatica Analyst Version 9.6 (VM based) and a bonus section.
The Complete Data Quality and Digital Transformation Course, led by instructor Bing Yu, focuses on key digital concepts such as data quality, data governance, chief data office, data strategy, metadata, and data profiling. As digital transformation continues to accelerate across industries, the course is highly relevant for individuals to improve their digital literacy and upskill themselves with data analytics skillsets. The course provides an understanding of the Chief Data Office function and its roles and responsibilities, as well as an end-to-end data quality management lifecycle and best practices critical to achieving the vision set out in data strategy. The course also covers advanced analytics use cases such as Artificial Intelligence, Machine Learning, Blockchain, and Robotic Automation.
The course covers a wide range of concepts including digital transformation, chief data officer, data governance, data stewardship, data quality, data architecture, operations intelligence, advanced analytics and data science, data quality objectives, data domains, ISO 8000, data profiling, metadata, business validation rules, data quality scorecard, tolerance level, root cause analysis, data cleansing, and data quality issue management. The course includes exercises to check understanding, as well as reading materials to better assimilate the concepts. Completion of the course will earn a certificate of completion.
The course “Improving data quality in data analytics & machine learning” is taught by Mike X Cohen. The course aims to educate learners on the significance of having high-quality data for making informed data-based decisions. The course covers various topics on data quality, including terminologies, data documentation, management, and research phases.
The course includes high-level strategies for ensuring high data quality, which covers a range of qualitative and quantitative methods for evaluating data quality, including visual inspection, error rates, and outliers. Additionally, the course provides Python code to help learners implement these visualizations and scoring methods using pandas, numpy, seaborn, and matplotlib.
Furthermore, specific data methods and algorithms for cleaning data and rejecting bad or unusual data are also covered in the course. Python code is also provided to help learners understand how to implement these procedures using pandas, numpy, seaborn, and matplotlib.
The course is suitable for data practitioners who want to understand both the high-level strategies and low-level procedures for evaluating and improving data quality. Additionally, managers, clients, and collaborators who want to understand the importance of data quality, even if they are not working directly with data, can benefit from the course.
The course is divided into several sections, including an introduction, course materials download (Python code), understanding why data quality matters, ensuring high data quality, assessing data quality, data transformations, outliers and missing data, and becoming a high-quality data scientist. A bonus section is also available.
This course, titled “Data Quality & Profiling with ETL Pentaho DI & DataCleaner,” offers a comprehensive overview of data quality improvement through profiling, cleansing, and automation using the popular ETL tool Pentaho Data Integration and Data Quality tool DataCleaner. Through 8 sessions, 43 lectures, and 103 minutes of content, learners will gain a fundamental understanding of key terminologies, basic concepts, and implementation techniques needed to build fully functional data quality implementations.
The course emphasizes the importance of data quality assurance for businesses to stay competitive and avoid losses. It also offers learners the confidence to tackle data-related challenges and opportunities in various formats and volumes.
The content includes comprehensive hands-on sessions, quizzes, and demo use cases to help learners test their knowledge and practice their skills. The downloadable files available in the last session of the course allow learners to practice at their own pace.
Upon completion of the course, learners will have the knowledge and confidence to implement fully functional automated data quality solutions in their projects. The course is divided into seven sections, covering topics such as building the foundation, introduction and installation of tools, working with DataCleaner, integrating DataCleaner with PDI, walk-through on demo use cases, and what’s next in data domain learning.
The DQS Training Course covers Microsoft SQL Server Data Quality Services for data cleansing and data deduplication with the use of Data Quality Client and SSIS tools. The course is taught by Vikas Munjal through an online video training format.
The course is divided into four main sections which include DQS Introduction, DQS Terminologies, DQS Architecture, and DQS Installation. The course also covers Data Cleansing, Data Source used for Demonstration, Domains and Knowledge Base, and a Cleansing Project. Additionally, an SSIS Package for Data Cleansing is also included.
Data Deduplication is also covered in the course with topics including Matching Policies and a Deduplication Project. The course goes on to cover Advanced DQS topics such as Term Based Relations, Composite Domains, Creating Regular Expressions Rules in a domain, Domain DataTypes, Import Knowledge Base & Domains, Export Knowledge Base & Domains, Open Delete Rename Unlock Knowledge Base & Projects, Purpose of each DQS Databases, Backup and Restore DQS Databases, DQS Configurations, and the DQS Activity Monitor.
The course is divided into four main sections: Introduction, Data Cleansing, Data Deduplication, and Advanced DQS. The course covers a wide range of topics, including DQS Terminologies, Matching Policies, and Composite Domains. The course is taught through an online video training format and is led by Vikas Munjal. The course is suitable for those interested in learning Microsoft SQL Server Data Quality Services for data cleansing and data deduplication with the use of Data Quality Client and SSIS tools.