https://github.com/lexiortiz/ibm-data-engineering-fundamentals

Notes, exercises, and projects from the IBM Data Engineering Fundamentals path via Verizon Skill Forward.
https://github.com/lexiortiz/ibm-data-engineering-fundamentals

data-engineering numpy pandas postegresql python sql

Last synced: 3 months ago
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Notes, exercises, and projects from the IBM Data Engineering Fundamentals path via Verizon Skill Forward.

• Host: GitHub
• URL: https://github.com/lexiortiz/ibm-data-engineering-fundamentals
• Owner: lexiortiz
• Created: 2025-02-10T18:16:18.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-22T18:05:04.000Z (over 1 year ago)
• Last Synced: 2025-02-22T19:20:09.682Z (over 1 year ago)
• Topics: data-engineering, numpy, pandas, postegresql, python, sql
• Language: Jupyter Notebook
• Homepage:
• Size: 146 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# IBM Data Engineering Fundamentals

## Overview

This repository contains my notes, exercises, and projects from the **IBM Data Engineering Fundamentals learning path**, offered through Verizon Skill Forward. The program covers foundational concepts, hands-on skills, and real-world applications in **data engineering**.

## Program Structure

The program consists of **six courses**, each focusing on different aspects of data engineering:

1. **Data Engineering Basics for Everyone** – Introduction to data engineering concepts, data lifecycle, and ecosystem.

2. **Python Basics for Data Science** – Fundamentals of Python, data structures, file handling, APIs, and libraries like Pandas & NumPy.

3. **Python for Data Engineering Project** – Hands-on projects using Python for data engineering workflows.

4. **Relational Database Basics** – Introduction to relational databases, MySQL, PostgreSQL, and IBM Db2.

5. **SQL for Data Science** – SQL queries, functions, stored procedures, and database transactions.

6. **SQL Concepts for Data Engineers** – Advanced SQL concepts like optimization, indexing, and complex queries.

## Goals

- Document my data engineering journey.

- Provide structured notes and practical examples.

- Build projects that demonstrate real-world skills.

- Share insights with others learning data engineering.

## Tools & Technologies

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffde57) ![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=CE123D) ![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=4D77CF) ![MySQL](https://img.shields.io/badge/mysql-f29111.svg?style=for-the-badge&logo=mysql&logoColor=00758f) ![Postgres](https://img.shields.io/badge/postgres-%23316192.svg?style=for-the-badge&logo=postgresql&logoColor=white)

- **Python**: Pandas, NumPy, API interactions, and ETL processes.

- **SQL**: MySQL, PostgreSQL, IBM Db2.

- **Data Repositories**: Databases, data warehouses, data lakes.

- **Big Data Concepts**.

## How to Use This Repo

- **Explore notes** for concept understanding.

- **Try exercises** to reinforce learning.

- **Check out projects** for real-world examples.

- **Refer to additional resources** for deeper learning.

## Connect with Me

I'm documenting my journey into **Data Engineering** and would love to connect!

- **GitHub**: [lexiortiz](https://github.com/lexiortiz)

- **LinkedIn**: [aortiz-dev](https://www.linkedin.com/in/aortiz-dev/)