Data Engineering Study Group

For the Data Engineering Specialization by Deeplearning AI

In this study group, we will follow the principles of "learning how to learn" by Barbara Oakley, along with other proven strategies for human-learning. Our goal is to help you develop strong data engineering and create an environment to help you to stay on track.

Each week, we will set specific goals and challenges to be completed. These challenges will be graded, and your scores will be displayed on a leaderboard, both here and in the Deeplearning AI community. This will allow you to track your consistency, showcase your skills, and demonstrate your abilities beyond just earning a certification.

First week

⚠ The main reason projects fail or qualified candidates are rejected is due to a lack of communication skills. Beginners often struggle with understanding requirements and articulating a plan that addresses business problems.

☑ In the first week of the Data Engineering Specialization, Joe Reis 🤓 guides you through how good data engineers are wired to communicate effectively with stakeholders (downstream users), from gathering information to developing a plan, with the business at the core of the solution.

Weekly Learning Approach:

For each learning session we suggest based on the review of how human actually learn using 5 simple steps in each session, it might not feel as natural at the begining but this will help you to succeed and improve your internal process of learning.

1. Review: Skim through the week's content and related chapters from the Data Engineering Fundamentals book. Note any questions or doubts.
2. Create a To-Do List: Outline key topics or skills you want to focus on.
3. Pomodoro Technique: Use the Pomodoro method to organize your study time.
4. Engage with the Material: Take notes in your own words and apply analogies that connect the concepts to your existing knowledge or interests.
5. Practical Application: Consider how you would apply this knowledge to a real-world project.

Study Group Structure & Leaderboard:

The study group session will have four major components

• Weekly Interactive Form: Complete a form with coding questions (similar to homework) using open-source datasets for hands-on practice. Share links to your public learning contributions and engage with peer questions.
• GitHub Repository: Maintain a central repository for your work, including solutions to the weekly coding exercises (many of which will involve real datasets).
• Weekly Live Sessions: Join virtual meetings (via Zoom or Google Meet) to discuss concepts and ask questions.
• Capstone Project: At the end of the course, you’ll work on an independent, open-ended project that integrates everything you've learned.

Form Components:

• 5 Coding Questions (required)
• 7 "Learning in Public" Links
• GitHub Repository Link
• Peer Question Answering (bonus points)