Course Overview
Microsoft Certified: Azure Data Scientist Associate is a mid-level certification that validates your ability to design, implement, and manage machine learning solutions on Microsoft Azure. It’s centered around using Azure Machine Learning, MLflow, and Azure AI services to build scalable, production-ready models.
This course is available as part of a Personal Learning Account (PLA). PLA is an initiative from the Welsh Government which offers people the chance to access free, part-time courses with flexible and convenient learning that fits around their existing lifestyle (subject to eligibility).
… anyone aged 19+, living in Wales and in employment. The usual salary limit of £34,303 does not apply to this course.
…professionals who want to prove their ability to build and manage machine learning solutions on Azure.
This course is delivered by virtual classroom. Virtual classrooms are equivalent to face-to-face classroom courses, but delivered in an online environment.
Course Duration: 4 Days
Throughout the course you will learn the following:
- Set up an Azure Machine Learning workspace.
- Run experiments and train machine learning models.
- Optimize and manage model training.
- Deploy and consume models in production.
- Monitor and maintain ML solutions.
- Use responsible AI and language models in Azure.
Delegates must have fundamental Microsoft Azure knowledge, experience coding in Python with libraries like Numpy and Pandas, and understanding of data science concepts including data preparation and machine learning model training.
You will need access to internet, a windows computer and webcam/microphone.
The PLA programme intends to provide quality career advice and guidance to participants before, during and after their learning.
Prior to being enrolled onto your PLA funded course, an individual learning plan will be discussed with you to ensure that the right learning has been considered.
This will include a general discussion on the following topics:
- formal education or qualifications in related fields.
- prior experience within the industry or field.
- career aspirations.
- dedication of time required.
