Course Description

The Digital Transformation is here! Equip yourself with the necessary skills to build data science applications utilizing Amazon Web Services (AWS) cloud platform. Learn the fundamentals of cloud computing, with a focus on the AWS cloud platform.

Students will work with AWS's python API to provision services and build a simple cloud-based data application. Collect and ingest data using EC2 and Kinesis Firehose, store data in S3 and visualize data using Quicksight.

This course is delivered in collaboration with WeCloudData.

Course Details

Learning Outcomes

By the completion of this course, successful students will be able to:

  • Describe the current cloud computing landscape and products and services offered by different vendors such as Microsoft Azure, Amazon Web Services, and Google Cloud
  • Explain AWS services such as EC2 (compute), S3 (storage), Kinesis (ingestion), Athena (analytics), Quicksight (visualization)
  • Apply the principles of cloud computing and the role it plays in modern data science and engineering
  • Create an AI application in cloud computing, using tools like AWS Python SDK boto3, AWS’s SageMaker, and Rekognition services
  • Construct data analytics pipelines by interacting with cloud services, such as AWS EC2, S3, Kinesis, Athena, Redshift, and AWS’s Python SDK boto3


Introduction to Cloud Computing

  • Introduction to AWS
  • Cloud Storage with S3
  • Cloud Compute with EC2
  • Managed Data Warehouse with Redshift
  • Serverless Analytics with Presto and Athena
  • Visualization in AWS with QuickSight
  • Programming for the cloud
  • Build your first cloud-based data application

Who is this course for?

  • Data scientists who want to learn how to move their data science applications to the cloud
  • IT and Engineering Professionals looking to gain cloud-based data science experience
  • Data and business analytics professionals
  • Recent graduates and academics in Computer Science


Software Requirements

This course is built around AWS solutions and services. To complete the lab activities and mini-project, you will require:

  • A development environment that includes Visual Studio Code or PyCharm
  • An AWS account (free tier services)


There are no mandatory prerequisites for this course. However, you are required to perform a self-assessment to ensure you meet the requirements to enroll.

Self-assessment for enrolment

A minimum of 1-year experience with the following skillsets: python programming, relational database, data visualization using python/excel, machine learning algorithms

Recommended Pre-requisites

  • DAT 110 Data Analytics and Visualization in Business
  • DAT 120 Practical Machine Learning for Business

Applies Towards the Following Program(s)

Enrol Now - Select a section to enrol in
Online Synchronous
5:00PM to 8:00PM
Jul 02, 2024 to Jul 23, 2024
Online Synchronous
8:00AM to 4:00PM
Jul 06, 2024 to Jul 27, 2024
Schedule and Location
Delivery Options
Course Fees
Flat Fee non-credit $1,495.00
Required Software
Zoom web conferencing software Laptop or computer installed with Windows or Mac OS A development environment that includes Visual Studio Code or PyCharm and AWS account (free tier services)
Reading List / Textbook

No textbook required.

Section Notes

Classes are held online in real time (Mountain Time) at the specified time and dates.

This course uses:

  • D2L Learning Management System
  • Zoom web conferencing software

This course is delivered in an online blended format, meaning that some classes are taught in a live virtual session using Zoom, and some work have to be completed in a designated online e-learning platform on your own time.

For the best experience, you will require access to a computer with Internet connection, a headset with speakers and microphone, webcam, and a monitor large enough to display multiple applications (or the use of two monitors). Your computer and internet connection should meet certain requirements. See the recommended requirements.

For more information, please visit our Online Learning Resources.

Students unfamiliar with online learning are encouraged to take our free Digital Skills for Learning Online course.

Unless otherwise stated, notice of withdrawal or transfer from a course must be received at least seven calendar days prior to the start date of the course.

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