Microsoft Azure Data Scientist Associate Certification Guide

Data scientists uncover insights from structured and unstructured data to help organizations improve revenue, reduce costs, increase business agility, improve customer experience, and develop new products. Data science teams typically seek to identify key data assets that can be transformed into data pipelines that power maintainable tools and solutions, from credit card fraud monitoring solutions used by banks to tools used to optimize the placement of wind turbines in wind farms.

The data scientist role is in high demand as organizations seek to become more data-driven and extract business insights from their data. In August 2021, research firm IDC predicted that global spending on big data and business analytics solutions would reach $215.7 billion in 2021 and continue to strengthen over the next five years, with an annual growth rate compound (CAGR) of 12.8% through 2025. According to executive recruiters Smith Hanley Associates, 2021 has been a banner year for hiring data scientists and it expects the trend to continue in 2022.

With data scientists earning salaries of up to $167,000 a year, averaging $117,212, according to Glassdoor, there’s no better time to step into the field and prove your mettle. To do this, you can obtain the Microsoft Certified certificate: Azure Data Scientist Associate. Here’s an overview of what certification entails and how to get it.

What is a Microsoft Certified: Azure Data Scientist Associate?

Microsoft Certified Azure Data Scientist Associates are subject matter experts who can plan and create a working environment for data science workloads on Microsoft Azure. They can run data experiments, train predictive models, and manage, optimize, and deploy machine learning models in production. The certification is for individuals who have expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.

Certification requires passing the Designing and Implementing a Data Science Solution on Azure certification exam. The exam measures the candidate’s ability to prepare, model, visualize, and analyze data, as well as deploy and maintain deliverables.

The Designing and Implementing a Data Science Solution on Azure exam

The Designing and Implementing a Data Science Solution on Azure exam costs $165 in the United States (price varies depending on the country in which the exam is invigilated). The exam measures the candidate’s ability to perform technical tasks, including:

• Azure resource management for machine learning (25%-30%)
• Running Experiments and Training Models (20%-25%)
• Deployment and operationalization of machine learning solutions (35% to 40%)
• Implement responsible machine learning (5%-10%)

Microsoft provides a detailed breakdown of the skills measured in each task.

Exam Prep Designing and Implementing a Data Science Solution on Azure

Candidates have two options to prepare for the exam: free online courses or paid instructor-led training.

For free courses, Microsoft recommends a series of four learning paths that cover the necessary skills:

• Create machine learning modules: This intermediate learning path provides a foundation in machine learning models, including exploring and analyzing data with Python, training and evaluating regression models, training and evaluating models classification, training and evaluation of clustering models, and training and evaluation of deep learning models. It consists of five modules and lasts 5 hours and 18 minutes.
• Microsoft Azure AI Fundamentals: explore visual tools for machine learning: This beginner’s learning path focuses on using Azure Machine Learning to build and publish models without writing code. It consists of four modules and lasts 3 hours and 29 minutes.
• Build and operate machine learning solutions with Azure Machine Learning: This intermediate learning path teaches how to use the Azure Machine Learning Python SDK to build and manage enterprise-ready ML solutions. It assumes experience in training machine learning models with Python and open source frameworks such as Scikit-Learn, PyTorch, and Tensorflow. It consists of 15 modules which take 10 hours and 47 minutes to complete.
• Build and operate machine learning solutions with Azure Databricks: This intermediate learning path focuses on using Azure Databricks to explore, prepare, and model data; and integrate with Azure Machine Learning. Like the previous learning path, it assumes experience using Python to explore data and train machine learning models. The learning path consists of 10 modules lasting 4 hours and 20 minutes.

Microsoft also offers two paid, instructor-led training courses for certification through its learning partners. Prices vary by country and learning partner. The courses are:

• Design and implement a data science solution on Azure: This three-day course covers how to leverage cloud-scale machine learning solutions using Azure Machine Learning. It explores how to leverage existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and monitoring of machine learning solutions in Microsoft Azure. The course is designed for data scientists with existing knowledge of Python and open source machine learning frameworks.
• Implementing a machine learning solution with Microsoft Azure Databricks: This one-day course covers how to use Azure Databricks to explore, prepare, and model data; and to integrate Databricks machine learning processes with Azure Machine Learning. It is designed for data scientists with existing knowledge of Python and open source machine learning frameworks.

Many practice tests and training resources are also available for the exam, including: