Professional Certificate in Trial Data Cleaning Guidelines

• Data Cleaning Basics: Introduction to data cleaning, identifying common data issues, and the importance of trial data cleaning.
• Data Quality Standards: Overview of data quality standards, measuring data quality, and implementing data quality assurance processes.
• Data Cleaning Techniques: Techniques for data cleaning, including data imputation, outlier detection, and data transformation.
• Data Cleaning Tools: Introduction to data cleaning tools, including OpenRefine, Trifacta, and Talend Data Quality.
• Data Cleaning Best Practices: Best practices for data cleaning, including version control, documentation, and testing.
• Data Integration and Cleaning: Techniques for integrating and cleaning data from multiple sources, including data normalization and data mapping.
• Data Cleaning for Data Analysis: Data cleaning techniques specific to data analysis, including data aggregation and data visualization.
• Data Cleaning for Machine Learning: Techniques for cleaning data for machine learning, including feature engineering and data preprocessing.
• Data Cleaning Case Studies: Real-world examples of trial data cleaning, including successes and failures, and lessons learned.