Python Demo Class

Description

🚀 45-Day Python + AWS Data Engineering Journey

1. Python

Description: Python is a high-level, versatile programming language used for scripting, automation, and building data pipelines.

Purpose: It’s the foundation of data engineering tasks, enabling fast development and integration.

Job Relevance: Strong Python skills are expected in every data engineering job.

2. Pandas

Description: Pandas is a Python library for data analysis and manipulation using DataFrames.

Purpose: It helps clean, transform, and explore structured data efficiently.

Job Relevance: Essential for pre-processing data before loading into storage or models.

3. Database

Description: Learn SQL, relational databases (PostgreSQL), and NoSQL databases (MongoDB, DynamoDB).

Purpose: Understand how to query, store, and manage structured data.

Job Relevance: Every data pipeline interacts with databases—this is a must-have skill.

4. Webscraping

Description: Extract data from websites using BeautifulSoup and requests.

Purpose: Used when data isn’t available via APIs.

Job Relevance: Useful for building POCs and extracting niche data from public sources.

5. AWS Cloud

Description: Learn AWS services like S3, Lambda, IAM, Glue, and CloudWatch.

Purpose: Cloud-based pipelines are scalable, secure, and fast.

Job Relevance: Cloud skills (especially AWS) are now mandatory in most data engineering jobs.

6. ETL Tools

Description: Tools like AWS Glue or Airflow for extracting, transforming, and loading data.

Purpose: Automate and schedule data workflows across systems.

Job Relevance: Core part of every data engineer’s responsibilities.

7. Data Warehousing

Description: Centralized data storage using Redshift, Snowflake, etc.

Purpose: Organize and prepare data for analytics and dashboards.

Job Relevance: Important for enabling business intelligence and reporting teams.

8. APIs & Integrations

Description: Work with REST APIs using Python for data exchange.

Purpose: Integrate external data sources into pipelines.

Job Relevance: Real-world pipelines often need API data ingestion.

9. Batch & Stream Processing

Description: Process data in batches or real-time using tools like Kafka or Kinesis.

Purpose: Handle large or streaming datasets effectively.

Job Relevance: Required for companies with high data velocity (e.g., finance, e-commerce).

10. Data Quality & Testing

Description: Apply checks and validations to ensure clean and reliable data.

Purpose: Prevent bad data from flowing downstream.

Job Relevance: High-quality data pipelines reduce business risks.

11. Data Visualization (Basic)

Description: Use Python or BI tools to visualize and interpret data.

Purpose: Quickly identify trends, errors, and insights.

Job Relevance: Helps in data storytelling and debugging outputs.

12. DevOps Tools

Description: Tools like Git, Docker, and CI/CD pipelines.

Purpose: Automate testing and deployment of data workflows.

Job Relevance: DevOps knowledge is a major plus in modern engineering roles.

13. PySpark

Description: Apache Spark’s Python API for large-scale distributed processing.

Purpose: Handle big data that doesn’t fit into memory.

Job Relevance: Widely used in big data pipelines in large enterprises.

14. Management Tools

Description: Use tools like Jira, Confluence, and Airflow UI for task tracking and orchestration.

Purpose: Helps you manage and track large projects and team efforts.

Job Relevance: Shows you're job-ready for enterprise workflows.

15. Interview Questions and Answers

Description: Practice real-world Python, SQL, and cloud-based scenario questions.

Purpose: Build confidence in communication and technical reasoning.

Job Relevance: Helps you clear interviews and stand out among other candidates.

16. Resume Preparation

Description: Learn how to build a project-focused, skill-rich resume.

Purpose: Show your potential to recruiters effectively.

Job Relevance: A great resume increases your chances of shortlisting by 70%.

17. Completed

Description: You’ve completed the journey!

Purpose: You’re now job-ready to apply for Data Engineer roles.

Job Relevance: Equipped with all the skills to land a job in the data field.