data analytics & cloud computing

Introduction

• Overview of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
• Introduction to Cloud Computing (IaaS, PaaS, SaaS)
• Importance of cloud in data analytics
• Use cases and industry examples

Cloud Computing Foundations

• Cloud service models (IaaS, PaaS, SaaS)
• Deployment models (Public, Private, Hybrid, Multi-cloud)
• Cloud providers overview (AWS, Azure, GCP)
• Basics of cloud storage and computing (e.g., S3, EC2, Azure Blob)

Data Analytics Process

• Data collection and ingestion
• Data cleaning and preprocessing
• Exploratory data analysis
• Feature engineering basics

Cloud-based Analytics Tools

• AWS: Athena, Redshift, Glue
• Azure: Synapse, Data Factory
• GCP: BigQuery, Dataflow
• Optional: Databricks, Snowflake

Data Pipelines in the Cloud

• ETL vs ELT concepts
• Workflow orchestration tools (Airflow, Glue, Data Factory)
• Batch vs real-time data processing
• Integration with cloud storage and databases

Data Visualization & Reporting

• Cloud-based BI tools (Power BI, Tableau Cloud, Google Data Studio)
• Connecting BI tools to cloud data sources
• Creating dashboards and automated reports

Security & Governance

• Data security in cloud platforms
• Identity and Access Management (IAM)
• Encryption practices
• Compliance and regulations (GDPR, HIPAA)
• Design a cloud-based analytics pipeline
• Ingest, process, analyze, and visualize real-world data
• Deploy using AWS, Azure, or GCP