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.
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
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
- DAT 110 Data Analytics and Visualization in Business
- DAT 120 Practical Machine Learning for Business
Applies Towards the Following Program(s)
- Big Data in Cloud : Required