Develop practical skills in cloud computing with Microsoft Azure and data analytics with SAS Viya for entry-level careers in data, analytics, and cloud. Designed for students and career switchers, this Specialization helps you understand how cloud services and analytics platforms work together to support the data and AI life cycle to make smarter business decisions.
You’ll start with Microsoft Azure fundamentals, including core cloud concepts, Azure services, architecture components, storage, virtual networking, virtual machines, scalability, and hands-on practice in the Microsoft Azure portal and sandbox environment. You’ll then build analytics skills with SAS Viya, exploring platform architecture, data management, visualization, model development, and coding in SAS or Python within an integrated analytics environment.
This program is a strong fit for learners interested in roles such as junior data analyst, analytics associate, cloud support, or cloud operations–adjacent positions. You’ll gain exposure to tools and technologies including Microsoft Azure, virtual networking, cloud storage, virtual machines, SAS Viya, SAS Studio, SAS Visual Analytics, and SAS Model Studio.
To be successful, learners should have basic computer literacy and familiarity with general technology concepts such as networking, storage, compute, and application support. No advanced coding is required to begin.
Projet d'apprentissage appliqué
Learners complete a comprehensive project that simulates real-world analytics workflows using SAS Viya supported by Microsoft Azure. Through an end-to-end diabetes case study, learners access, prepare, analyze, visualize, and model healthcare data to answer meaningful questions. The case study guides learners through importing and managing data, assessing data quality and privacy, applying data transformations, generating insights with interactive reports, and building predictive models. By working with authentic data and industry-style tools, learners develop practical skills in data exploration, reporting, and machine learning that directly translate to workplace scenarios. These projects result in tangible work samples that learners can showcase on a resume and confidently discuss during interviews as evidence of real-world problem-solving experience.
