AI: Python and Pandas for Data Engineering

by Pragmatic AI Labs

Python and Pandas for Data Engineering

Course Description

Are you ready to embark on an exciting journey into the world of data engineering? Our comprehensive course, "Python and Pandas for Data Engineering," is designed to equip you with the essential skills needed to excel in this rapidly growing field. This course is your gateway to mastering Python programming and the powerful Pandas library, two fundamental tools in the data engineer's toolkit.

What Students Will Learn

Python fundamentals and advanced concepts
Setting up and managing Python environments
Data manipulation and analysis using Pandas
Exploration of alternative data structures like NumPy arrays and PySpark DataFrames
Proficiency in development tools such as Vim, Visual Studio Code, and Git
Real-world application of data engineering skills

Pre-requisites

This course is designed for beginners and those with some programming experience. No specific prerequisites are required, making it accessible to anyone interested in data engineering.

Course Coverage

Python environment setup and package management
Core Python syntax and data structures
Pandas DataFrames for data manipulation and analysis
Alternative data structures for big data handling
Version control with Git and GitHub
Development environments (Vim and Visual Studio Code)
Hands-on exercises and projects to reinforce learning

Who This Course Is For

Aspiring data engineers
Software developers looking to transition into data engineering
Data analysts seeking to enhance their technical skills
Students interested in pursuing a career in data science or engineering
Professionals in any field looking to gain valuable data manipulation skills

Real-World Application of Skills

Efficiently process and analyze large datasets
Automate data workflows and pipelines
Contribute to data-driven decision-making processes in organizations
Collaborate effectively on data projects using version control
Develop robust and scalable data solutions
Enhance their career prospects in the growing field of data engineering

Syllabus

The course is divided into four comprehensive modules:

1. Getting Started with Python (14 hours)

2. Essential Python (11 hours)

3. Data in Python: Pandas and Alternatives (12 hours)

4. Python Development Environments (13 hours)

Each module contains a mix of video lectures, readings, quizzes, and hands-on labs to ensure a well-rounded learning experience. The course concludes with a cumulative quiz and final challenges to solidify your understanding of the material.

By the end of this course, you'll have the confidence and skills to tackle real-world data engineering challenges using Python and Pandas. Don't miss this opportunity to transform your career and become a proficient data engineer!

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