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Data Analyst Mastery
What you'll learn
Learning Path and Outcome
Python Proficiency: Automate ETL workflows and interact with Snowflake.
SQL Mastery: Query, optimize, and transform data within Snowflake.
ETL Expertise: Build and automate data pipelines using dbt and Python.
Snowflake Knowledge: Master data warehousing concepts and prepare for certifications.
Job Readiness: Gain practical experience through projects and interview prep.
Azure Data Engineering: End-to-End Track (60-80 Hours)
What you'll learn
Python and SQL Mastery: For data manipulation and querying
ETL Expertise: Build pipelines using Azure Data Factory
Real-Time Streaming: Work with Event Hubs and Databricks
Data Warehousing Skills: Create optimized data warehouses with Synapse Analytics
Monitoring and Automation: Use Azure Monitor and Automation tools
Data Engineering : Python, SQL, Snowflake, and ETL Focus Mastery
What you'll learn
Learning Path and Outcome
Python Proficiency: Automate ETL workflows and interact with Snowflake.
SQL Mastery: Query, optimize, and transform data within Snowflake.
ETL Expertise: Build and automate data pipelines using dbt and Python.
Snowflake Knowledge: Master data warehousing concepts and prepare for certifications.
Job Readiness: Gain practical experience through projects and interview prep.
Data Engineering with AWS
What you'll learn
Fundamentals of cloud computing and AWS services
Setting up and managing an AWS account
Core AWS services: EC2, S3, RDS, VPC
AWS security best practices and identity management (IAM)
Deploying and scaling applications on AWS
AWS cost optimization and management
Introduction to serverless computing with AWS Lambda
Basic AWS networking concepts
AWS monitoring and logging with CloudWatch
