Microsoft Azure Data Scientist Associate certification guide

Data scientists discover insights from structured and unstructured data to help organizations improve revenue, reduce costs, increase business agility, improve customer experiences, and develop new products. Data scientist teams typically seek to identify key data assets that can be turned into data pipelines that feed 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 forecast worldwide spending on big data and business analytics solutions would reach $215.7 billion in 2021 and continue to gain strength over the next five years, with a compound annual growth rate (CAGR) of 12.8% through 2025. According to executive recruiters Smith Hanley Associates, 2021 was a banner year for data scientist hires and it expects the trend to continue in 2022.

With data scientists commanding salaries up to $167K per year, with an average of $117,212, according to Glassdoor, there is no better time to step into the field and prove your mettle. One way of doing so is to achieve the Microsoft Certified: Azure Data Scientist Associate certificate. Here’s a look at what the certification entails and how to earn 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 into production. The certification is intended for individuals who have expertise in applying data science and machine learning to implement and run machine learning workloads on Azure.

The 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, and to 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 US (the price varies based on the country in which the exam is proctored). The exam measures the candidate’s ability to perform technical tasks, including:

• Managing Azure resources for machine learning (25%-30%)
• Running experiments and training models (20%-25%)
• Deploying and operationalizing machine learning solutions (35%-40%)
• Implementing responsible machine learning (5%-10%)

Microsoft offers a thorough breakdown of the skills measured within each task.

Preparing for the Designing and Implementing a Data Science Solution on Azure exam

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

For the 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 data exploration and analysis with Python, training and evaluating regression models, training and evaluating classification models, training and evaluating clustering models, and training and evaluating deep learning models. It consists of five modules and takes 5 hours and 18 minutes to complete.
• Microsoft Azure AI Fundamentals: Explore visual tools for machine learning: This beginner learning path focuses on how to use Azure Machine Learning to create and publish models without writing code. It consists of four modules and takes 3 hours and 29 minutes to complete.
• 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 create 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 that 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 that take 4 hours and 20 minutes to complete.

Microsoft also provides two instructor-led, paid training courses for the certification via its learning partners. The prices vary by country and learning partner. The courses are:

• Designing and Implementing a Data Science Solution on Azure: This three-day course covers how to operate machine learning solutions at cloud scale 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 machine learning solution monitoring 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’s designed for data scientists with existing knowledge of Python and open source machine learning frameworks.

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