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Practical Data Science with Amazon SageMaker Course Overview

Practical Data Science with Amazon SageMaker Course Overview

The Practical Data Science with Amazon SageMaker course is a comprehensive program designed to teach learners the ins and outs of machine learning (ML) with a focus on using Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. The course covers various aspects of ML, from the basics to more advanced techniques.

Starting with an introduction to the types of ML, job roles, and the ML Pipeline, the course progresses to Data Preparation and the use of SageMaker. Learners will engage in practical exercises, such as launching a Jupyter Notebook and preparing datasets, and delve into Data Analysis and visualization. The course emphasizes hands-on learning with exercises for Model Training, evaluation, and Hyperparameter Tuning.

Modules on deployment, Production Readiness, and Cost Analysis of errors are also included, ensuring that students understand the full lifecycle of ML model development. With a focus on Practical Data Science with Amazon SageMaker, this course is ideal for those looking to apply ML in real-world scenarios, leveraging SageMaker's architecture and features to streamline the process.

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  • Live Training (Duration : 8 Hours)
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  • Includes Official Coursebook
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Koenig Solutions Private Limited is a finalist for the AWS Training Partner of the Year (APJ) 2024. winner of the AWS Training Partner of the Year Award – APJ Region for 2022. The award was given in a glittering ceremony in Las Vegas attended by Subo AWS Customer Obsession Award in August 2022.

Course Prerequisites

Certainly! To ensure that students are adequately prepared for the Practical Data Science with Amazon SageMaker course and can get the most out of the training, the following minimum prerequisites are suggested:


  • Basic understanding of machine learning concepts, including familiarity with the types of machine learning (supervised, unsupervised, and reinforcement learning).
  • Fundamental knowledge of Python programming, as Python is commonly used for scripting in data science tasks and exercises within the SageMaker environment.
  • Experience with data handling and manipulation using Python libraries such as pandas and NumPy.
  • Some familiarity with basic data visualization techniques and tools, which could include libraries such as matplotlib or seaborn in Python.
  • An understanding of the general data science workflow, from data preparation and analysis to model training and evaluation.
  • Awareness of AWS cloud services is beneficial, although not strictly necessary, as the course will provide an introduction to Amazon SageMaker.
  • No prior experience with Amazon SageMaker is required as the course will cover this from an introductory level.

Students who meet these prerequisites are more likely to successfully grasp the course content and apply the skills learned in the Practical Data Science with Amazon SageMaker training.


Target Audience for Practical Data Science with Amazon SageMaker

Practical Data Science with Amazon SageMaker is a comprehensive course designed for professionals seeking to leverage ML in cloud environments.


  • Data Scientists and Analysts
  • Machine Learning Engineers
  • Software Developers interested in ML deployment
  • IT Professionals looking to expand into data science roles
  • Business Analysts wanting to understand ML applications
  • Technical Project Managers overseeing ML projects
  • Cloud Engineers and Architects focusing on AWS services
  • Professionals seeking to understand customer churn analytics
  • Data Engineers who want to prepare and manage data for ML
  • AI/ML Consultants advising on model training and tuning
  • Students pursuing careers in data science and machine learning


Learning Objectives - What you will Learn in this Practical Data Science with Amazon SageMaker?

Introduction to the Course's Learning Outcomes and Concepts Covered

In the Practical Data Science with Amazon SageMaker course, students will learn to build, train, tune, and deploy machine learning models using AWS SageMaker.

Learning Objectives and Outcomes

  • Understand the different types of machine learning (supervised, unsupervised, and reinforcement learning) and their applications.
  • Identify various job roles within the machine learning field and the responsibilities associated with them.
  • Gain knowledge of the steps involved in the machine learning pipeline, from data preparation to model deployment.
  • Learn to define training and test datasets and get introduced to Amazon SageMaker's capabilities and environment.
  • Formulate real-world problems into machine learning tasks using the example of customer churn and prepare datasets for analysis.
  • Acquire skills in data analysis and visualization, including cleaning data and understanding the relationships between features.
  • Master training and evaluating machine learning models with SageMaker, using algorithms such as XGBoost, and understand how to set hyperparameters effectively.
  • Learn to automatically tune models in SageMaker for optimal performance and efficiency.
  • Deploy models into production with best practices, including A/B testing and auto-scaling, to handle varying loads.
  • Understand the relative cost of errors in machine learning models and learn to manage trade-offs between different types of errors.

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