Masterclass Certificate in Data Quality with R

• Data Quality Fundamentals with R → Understanding the importance of data quality and how R can be used to assess and improve it.
• Data Cleaning in R → Techniques for cleaning and preparing data for analysis, including handling missing values and outliers.
• Data Profiling with R → Assessing data quality through data profiling, including exploring data distributions and identifying anomalies.
• Data Validation with R → Implementing data validation rules and checks to ensure data quality.
• Data Matching and Linking in R → Techniques for matching and linking data records to improve data quality and consistency.
• Data Quality Metrics and Reporting in R → Measuring and reporting on data quality using R, including creating visualizations and dashboards.
• Data Governance and Management in R → Understanding the role of data governance and management in ensuring data quality.
• Advanced Data Quality Techniques in R → Exploration of advanced techniques for improving data quality, such as machine learning and natural language processing.
• Case Studies and Real-World Examples → Examination of real-world examples and case studies to demonstrate the application of data quality techniques in R.