A data scientist is a person who extracts actionable insights from data by using programming, statistics, machine learning, and domain knowledge.
Explore the evolving role of a data scientist within the Microsoft Azure ecosystem. Learn to automate data infrastructure deployment using Azure Resource Manager (ARM) templates or Terraform for infrastructure-as-code, ensuring consistent, scalable, and reproducible environments for data science projects. Leverage Azure Synapse Analytics and Azure Machine Learning to develop advanced machine learning models and data-driven applications that deliver highly personalized, actionable insights tailored to specific business challenges. Build adaptive, real-time data ingestion and streaming analytics solutions using Azure Event Hubs and Azure Stream Analytics to support continuous learning and decision-making. Implement intelligent enterprise search and knowledge discovery with Azure Cognitive Search, and perform sophisticated relationship and graph analytics by modeling complex data in graph databases using Azure Cosmos DB’s Gremlin API. Develop end-to-end machine learning workflows, from data preparation and feature engineering to model training, deployment, and monitoring, empowering you to solve complex problems with data and drive impactful business outcomes.
Build adaptive, real-time data processing and streaming analytics solutions using Azure Event Hubs and Azure Stream Analytics.
Develop and deploy scalable data science workflows using Azure Data Factory for data orchestration and Azure Functions (serverless computing) or containerized solutions with Azure Kubernetes Service (AKS) for model deployment and automation. Seamlessly integrate powerful AI and cognitive services like Azure Cognitive Search (enterprise search), Azure Text Analytics (natural language processing), and Azure Form Recognizer (document extraction) into your data science pipelines using simple API calls.
It is designed to validate the knowledge, skills, and competencies of individuals in a specific area of study or professional field. The certification program is meticulously designed to ensure that candidates have acquired a comprehensive understanding of the subject matter. It encompasses both theoretical knowledge and practical application, allowing candidates to demonstrate their expertise in real-world scenarios.
Strategically orchestrate data services using an Azure Data Lake built on Azure Blob Storage as a centralized repository for your data assets. Promote collaboration between data engineers and analysts using tools such as Azure Synapse Studio, Azure Data Factory, and Azure DevOps for unified data workflows, version control, and continuous integration/continuous deployment (CI/CD). Learn to leverage Microsoft’s rich data analytics ecosystem to address complex business problems and enhance data-driven decision-making. Gain hands-on experience in data ingestion, transformation, feature engineering, and building analytical models using languages like SQL, Python, and C# in Azure environments. Expand your global perspective by deploying analytics solutions across Azure’s global regions and availability zones to ensure performance, compliance, and resiliency. Solve complex challenges by integrating Azure’s analytics offerings with powerful big data tools such as Azure Synapse Analytics, Azure Databricks, and Azure Stream Analytics, crafting comprehensive, client-focused data strategies.
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