Practical Data Science with Amazon SageMaker

This course provides hands-on experience solving a real-world machine learning use case using Amazon SageMaker. Participants walk through the full data science workflow—from data exploration and visualization to feature engineering, model training, tuning, deployment, and evaluation. Using a practical business scenario such as churn prediction, the course emphasizes producing actionable insights and deploying models into production. By the end of the course, learners will be able to confidently apply Amazon SageMaker to build, evaluate, and operationalize machine learning models in real-world environments.

• Analyze and visualize datasets to understand business problems
• Prepare datasets and perform feature engineering for machine learning
• Select appropriate Amazon SageMaker algorithms based on problem requirements
• Train, evaluate, and automatically tune machine learning models
• Deploy trained models for inference using Amazon SageMaker
• Assess model performance and understand the business impact of prediction errors
• Think critically about machine learning results and model trade-offs

• Technical professionals at an intermediate level
• Data analysts and aspiring data scientists
• Machine learning practitioners learning to use Amazon SageMaker
• Developers and engineers interested in applying ML to business problems

• Working knowledge of a programming language
• Basic familiarity with data analysis or machine learning concepts is recommended