This module introduces the core AWS data storage services, including S3, EBS, and RDS, and demonstrates how to create and manage storage resources. Learners will explore access control, encryption, and best practices for securing and organizing data in the AWS cloud. Practical exercises guide you through configuring storage and understanding the differences between storage types.

This module introduces key AWS services for migrating, storing, and processing data, including hands-on experience with AWS Glue, Kinesis Data Firehose, and DataSync. Learners will explore how to move data between storage solutions, transform data for analytics, and process large datasets using managed AWS tools. By the end, you'll understand practical workflows for real-world data migration and processing scenarios.

This module guides learners through essential data preparation techniques, including transforming categorical and numerical features, handling outliers and unbalanced datasets, and processing text data for machine learning. You will explore practical methods such as encoding, normalization, standardization, and TF-IDF to ensure your data is ready for modeling. By the end, you'll be equipped to address common data challenges and improve the quality of your machine learning pipelines.

This module introduces the principles of effective data visualization and the importance of clear communication in presenting analytical findings. Learners will explore foundational techniques for understanding and visually representing data to ensure insights are accessible and impactful.

This module guides learners through the practical application of key machine learning algorithms, including linear regression, classification, clustering, and dimensionality reduction. You will gain hands-on experience building models from scratch, evaluating their performance, and understanding essential concepts such as parsimony, stationarity, and cluster quality. By the end, you'll be equipped to select and implement appropriate algorithms for various data science tasks.

This module guides learners through the process of assessing machine learning model performance using key evaluation metrics. You will explore how to interpret precision, recall, F1 score, and AUC, and learn strategies for optimizing models based on these metrics.

This module introduces key AWS services for artificial intelligence and machine learning applications, including tools for text-to-speech, speech-to-text, natural language processing, translation, document extraction, and chatbot creation. Learners will discover how to leverage these managed services to solve real-world business challenges and automate complex workflows.

This module guides learners through the practical aspects of building, training, and deploying machine learning models using Amazon SageMaker. You will explore data storage formats, select appropriate instance types, configure scalability, secure your environment, and leverage debugging tools to monitor and optimize your models.

This module guides you through the process of configuring and deploying machine learning models using AWS services. You will learn how to set up event triggers and finalize deployment settings for Lambda functions, enabling automated and scalable model inference. By the end, you'll be equipped to operationalize your models in real-world environments.