9 Sections
107 Lessons
10 Weeks
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Python
38
1.1
Basics of programming logic
1.2
Understanding algorithms and flowcharts
1.3
Overview of Python as a programming language
1.4
Setting Up Python Environment
1.5
Installing Python
1.6
Working with Python IDEs
1.7
(Integrated Development Environments)
1.8
Writing and executing the first Python script
1.9
Python Basics
1.10
Variables and data types
1.11
Basic operations (arithmetic, comparison, logical)
1.12
Input and output (print, input)
1.13
Control Flow
1.14
Conditional statements (if, elif, else)
1.15
Loops (for, while)
1.16
Break and continue statements
1.17
Functions in Python
1.18
Defining functions
1.19
Parameters and return values
1.20
Scope and lifetime of variables
1.21
Lists and Tuples
1.22
Creating and manipulating lists
1.23
Slicing and indexing
1.24
Working with tuples
1.25
Dictionaries and Sets
1.26
Understanding dictionaries
1.27
Operations on sets
1.28
Use cases for dictionaries and sets
1.29
File Handling
1.30
Reading and Writing Files
1.31
Opening and closing files
1.32
Reading from and writing to files
1.33
Working with different file formats (text, CSV)
1.34
Error Handling and Modules
1.35
Error Handling
1.36
Introduction to exceptions
1.37
Try, except, finally blocks
1.38
Handling different types of errors
AWS Data Storage
4
2.1
Amazon S3 (Simple Storage Service) for scalable object storage
2.2
Amazon RDS (Relational Database Service) for managing relational databases
2.3
Amazon DynamoDB for NoSQL database storage
2.4
Amazon Redshift for data warehousing and analytics
AWS Data Processing
3
3.1
AWS Glue for ETL (Extract, Transform, Load) and data preparation
3.2
Amazon EMR (Elastic MapReduce) for processing large amounts of data using Hadoop, Spark, or other big data frameworks
3.3
Amazon Kinesis for real-time data streaming and processing
MySQL
27
4.1
SQL Advance Queries
4.2
SQL Data Models
4.3
SQL
4.4
Overview of Azure Data
4.5
Factory and its features
4.6
Comparison with other data integration services
4.7
Getting Started with Azure Data Factory
4.8
Setting up an Azure Data Factory instance
4.9
Exploring the Azure Data Factory user interface
4.10
Data Movement in Azure Data Factory
4.11
Copying data from various sources to destinations
4.12
Transforming data during the copy process
4.13
Data Orchestration in Azure Data Factory
4.14
Creating and managing data pipelines
4.15
Monitoring and managing pipeline runs
4.16
Data Integration with Azure Data Factory
4.17
Using datasets and linked services
4.18
Building complex data integration workflows
4.19
Data Transformation in Azure Data Factory
4.20
Using data flows for data transformation
4.21
Transforming data using mapping data flows
4.22
Integration with Azure Services
4.23
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
4.24
Using Azure Data Factory with Azure Databricks for advanced data processing
4.25
Monitoring and Management
4.26
Monitoring pipeline and activity runs
4.27
Managing and optimizing data pipelines for performance
Data Pipeline Orchestration
27
5.1
SQL Advance Queries
5.2
SQL Data Models
5.3
SQl
5.4
Overview of Azure Data
5.5
Factory and its features
5.6
Comparison with other data integration services
5.7
Getting Started with Azure Data Factory
5.8
Setting up an Azure Data Factory instance
5.9
Exploring the Azure Data Factory user interface
5.10
Data Movement in Azure Data Factory
5.11
Copying data from various sources to destinations
5.12
Transforming data during the copy process
5.13
Data Orchestration in Azure Data Factory
5.14
Creating and managing data pipelines
5.15
Monitoring and managing pipeline runs
5.16
Data Integration with Azure Data Factory
5.17
Using datasets and linked services
5.18
Building complex data integration workflows
5.19
Data Transformation in Azure Data Factory
5.20
Using data flows for data transformation
5.21
Transforming data using mapping data flows
5.22
Integration with Azure Services
5.23
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
5.24
Using Azure Data Factory with Azure Databricks for advanced data processing
5.25
Monitoring and Management
5.26
Monitoring pipeline and activity runs
5.27
Managing and optimizing data pipelines for performance
Data Analytics
2
6.1
Amazon Athena for querying data in S3 using SQL
6.2
Amazon QuickSight for business intelligence and data visualization
Security and Governance
2
7.1
Implementing security best practices for data on AWS
7.2
Managing data governance policies on AWS
Monitoring and Optimization
2
8.1
Monitoring data pipelines and optimizing performance and costs
8.2
Using AWS tools for monitoring and optimizing data processing
Hands-on Labs and Projects
2
9.1
Hands-on experience with AWS services for data engineering
9.2
Building data pipelines, processing data, and analyzing data using AWS
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