Candidates for this exam should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Microsoft Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. They run data experiments and train predictive models. In addition, they manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks. Provides several interactive ways to get an introduction to classic machine learning. These learning paths will get you productive on their own, and also are an excellent base for moving on to deep learning topics.

From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy to digest conceptual content and interactive Jupyter notebooks, all without leaving your browser.

Unsurprisingly, the role of a data scientist primarily involves exploring and analyzing data. The results of an analysis might form the basis of a report or a machine learning model, but it all begins with data, with Python being the most popular programming language for data scientists.

Usually, a data analysis project is designed to establish insights around a particular scenario or to test a hypothesis.

For example, suppose a university professor collects data from their students, including the number of lectures attended, the hours spent studying, and the final grade achieved on the end of term exam. The professor could analyze the data to determine if there is a relationship between the amount of studying a student undertakes and the final grade they achieve. The professor might use the data to test a hypothesis that only students who study for a minimum number of hours can expect to achieve a passing grade.