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Practical Data Science with Amazon SageMaker is a Program

Practical Data Science with Amazon SageMaker


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Full program description

Practical Data Science with Amazon SageMaker

Course Overview

NTUC LearningHub Course Code: AWSPDS

In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.

For those interested in a guided view of the machine learning (ML) pipeline, this intermediate-level course walks technical learners through the stages of a typical data science process for ML. These stages include analysing and visualising a data set; preparing the data and feature engineering; and the practical aspects of model building, training, tuning, and deployment with Amazon SageMaker.

Built by the experts at AWS and delivered by AWS-accredited instructors, this course is the industry's only official advanced developing course for the AWS Cloud.

Who Should Attend

This course is intended for:

  • Data science practitioners
  • Machine learning practitioners
  • Developers and engineers
  • Systems architects

Course Duration

1 Day / 8 hours

Course Outline

  • Analysing and visualising a dataset
  • Preparing the data and feature engineering
  • Model building, training, tuning and deployment

Course Objectives

In this course, you will learn how to:

  • Apply Amazon SageMaker to a specific use case and dataset
  • Practice all the steps of the typical data science process
  • Visualise and understand the dataset
  • Explore how the attributes of the dataset relate to each other
  • Prepare the dataset for training
  • Use built-in algorithms
  • Train models with Amazon SageMaker using built-in algorithms
  • Explore results and performance of the model, and demonstrate how it can be tuned and executed outside of SageMaker
  • Run predictions on a batch of data with Amazon SageMaker
  • Deploy a model to an endpoint in Amazon SageMaker for real-time predictions
  • Learn how to configure an endpoint for serving predictions at scale
  • Understand Hyperparameter Optimisation (HPO) with Amazon SageMaker to find optimal model parameters
  • Understand how to perform A/B model testing using Amazon SageMaker
  • Perform the domain-specific cost of errors analysis to further tune the model threshold in order to maximise model utility expressed in financial terms


We recommend that attendees of this course have the following prerequisites:

  • Experience with Python programming language
  • Familiarity with NumPy and Pandas Python libraries is a plus
  • Familiarity with fundamental machine learning algorithms
  • Familiarity with productionising machine learning models

Medium of Instruction & Trainer

Medium of Instruction: English



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Original Course Fee



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