Prerequisite: None

One challenge in the machine learning lifecycle is understanding the problem or opportunity, the next challenge is acquiring, understanding, and preparing data for the modeling phase. This step in the machine learning process is estimated take more than 50% of the time allotted for a machine learning project. This course addresses how to translate the problem statement, identifying data sources, exploring the data for relationships and recognize patterns, identifying the starting inputs for the model, preparing data, and validating it for the model fitting process.

You Will Learn

• Understand the data science project methodology
• Understand data source identification (align with problem model)
• Evaluate data findings to determine and validate modeling techniques
• Review feature selection techniques
• Understand data preparation techniques (cleansing, formatting, and blending approaches)
• Plan for data pipelines (proactive and reusable data preparation)
• Understand data visualization techniques for data understanding and data preparation

Geared To

• Business Analysts
• Project Managers
• New Data Scientists
• New Data Engineers
• Citizen Data Scientists