Data cleaning frameworks and techniques – Data Professionals

Mastering Precision and Reliability in Data: Advanced Data Cleaning Frameworks and Techniques for Data Professionals

Created by: KultureHire Education
Instructor at Udemy

Created by: Kadhiravan Jayachandiran
Founder @KultureHire & YourExcelGuy

What Will I Learn?

• Learn how to preprocess and clean datasets effectively before analysis.
• Acquire skills to handle missing data, outliers, and inconsistencies.
• Understand best practices for cleaning and preparing data for storage and analysis.
• Explore tools and frameworks for building scalable and automated data cleaning pipelines.
• Gain skills to ensure data quality and reliability for making informed business decisions.
• Learn techniques to validate and clean data according to business rules.
• Acquire knowledge on preparing datasets for machine learning models.
• Understand the impact of data quality on model performance.
• Learn strategies to clean and maintain data integrity within databases.
• Understand how to handle anomalies and inconsistencies in stored data.
• Acquire skills for cleaning and preparing datasets for research purposes.
• Learn techniques to enhance the reliability of research findings.
• Gain practical skills in data cleaning for personal or small-scale projects.
• Understand common challenges and solutions in real-world data scenarios.
• Develop an awareness of the impact of data quality on organizational decision-making.

Requirements

• Basic Data Knowledge

Target audience

• Data Analysts and Scientists: Individuals responsible for analyzing and extracting insights from data will benefit from learning data cleaning techniques
• Data Engineers: Professionals involved in the design and construction of data architecture, pipelines, and systems
• Database Administrators: Those responsible for managing databases can learn how to identify and address issues related to data quality, ensuring the integrity
• Data Quality Managers: Professionals focused on maintaining and improving overall data quality within an organization
• Data Governance Professionals: Those involved in implementing and enforcing data governance policies
• Data Stewards: Individuals responsible for managing and curating specific datasets within an organization
• Machine Learning and AI Engineers: Practitioners working on machine learning and artificial intelligence projects
• Business Analysts: Professionals analyzing business data to make strategic decisions will find this course valuable for improving the accuracy
• Researchers and Academics: Researchers and academics working with datasets in various fields can enhance the quality of their research