Undergraduate Certificate in DevOps Practices for Data Science in the Cloud
-- ViewingNowThe Undergraduate Certificate in DevOps Practices for Data Science in the Cloud is a comprehensive course designed to meet the growing industry demand for professionals with expertise in DevOps and data science. This certificate program equips learners with essential skills to excel in today's dynamic and data-driven cloud computing landscape.
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⢠Version Control with Git: an introduction to using Git for version control in DevOps, including basic commands, branching, and merging.
⢠Cloud Fundamentals: an overview of cloud computing concepts, including Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS), with a focus on public cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
⢠DevOps Tools for Data Science: an exploration of tools commonly used in DevOps for data science, such as Docker, Kubernetes, and Jenkins, including containerization, orchestration, and continuous integration and delivery (CI/CD).
⢠Infrastructure as Code (IaC): an introduction to IaC concepts and tools, such as Terraform, Ansible, and Chef, including writing and deploying code to automate infrastructure provisioning and configuration.
⢠Monitoring and Logging: an overview of monitoring and logging tools and best practices, including Prometheus, Grafana, Elasticsearch, and Logstash, for tracking and analyzing system performance and errors in cloud-based data science environments.
⢠Security in DevOps: an exploration of security best practices and tools for DevOps, including secrets management, vulnerability scanning, and compliance, with a focus on securing cloud-based data science workflows.
⢠Data Pipelines and Workflows: an introduction to building and managing data pipelines and workflows in cloud-based DevOps environments, including orchestration tools such as Apache Airflow and AWS Step Functions.
⢠Cloud-Native Machine Learning: an exploration of cloud-native machine learning frameworks and platforms, such as TensorFlow, PyTorch, and Kubeflow, including deploying and scaling machine learning models in cloud-based DevOps environments.
⢠DevOps Culture and Collaboration: an overview of the cultural and collaboration aspects of DevOps, including agile methodologies, communication, and collaboration best practices, with a focus on building effective data science teams in cloud
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