100-Day Python Learning Journey: A Deep Dive into Data Engineering and Beyond 🚀🐍

Hey there, Medium community! 👋 I’m excited to embark on a 100-day Python learning journey, and I’m inviting you all to join me on this adventure of exploration and growth. Whether you’re a coding newbie or an experienced programmer, there’s always something new to discover in the world of Python and data engineering. So, let’s dive in together! 🎉

Photo by Jaromír Kavan on Unsplash

The Roadmap

Over the next 100 days, I’ve planned an exhilarating roadmap covering various topics, from mastering Python basics to exploring advanced data engineering techniques. Let’s look at the learning journey that lies ahead:

Group 1: Python Basics and Fundamentals

1. Introduction to Python: Install Python and write your first “Hello, World!” code.
2. Variables and Data Types: Perform data type conversions and variable assignments.
3. Control Flow and Loops: Use conditionals and loops to control program flow.
4. Functions and Modules: Create and import functions and modules in Python.

Group 2: Data Manipulation Essentials

1. File Handling and Input/Output: Read from and write to files in different formats.
2. Error Handling: Implement error handling mechanisms in Python.
3. Regular Expressions: Use regular expressions to extract patterns from text

Group 3: Data Processing and Analysis

1. Working with Databases: Connect to databases and execute SQL queries in Python.
2. Data Processing with Pandas: Perform data manipulation and analysis using Pandas.
3. Data Cleaning and Transformation: Clean and transform data using Pandas.

Group 4: Data Interaction and APIs

1. Data Serialization and Storage: Serialize and store data in various formats.
2. Web Scraping: Scrap data from websites using Python.
3. Working with APIs: Retrieve data from web APIs using Python

Group 5: Data Transformation Strategies

1. Data Extraction and Transformation: Extract and transform data from different sources.
2. Relational Database Concepts: Understand relational database concepts and SQL.
3. Database Design and Normalization: Design databases and normalize tables.

Group 6: SQL Mastery and Warehousing

1. SQL Queries and Optimization: Write efficient SQL queries and optimize performance.
2. Introduction to Data Warehousing: Learn about data warehousing principles and techniques.
3. ETL (Extract, Transform, Load) Processes: Implement ETL processes using Python.

Group 7: Data Workflow and Big Data

1. Data Pipelines and Workflow Management: Build and manage data pipelines using Apache Airflow.
2. Data Serialization Formats and Compression: Work with data serialization formats and compression.
3. Big Data Concepts and Technologies: Understand big data concepts and popular technologies.

Group 8: Advanced Data Processing Technologies

1. Introduction to Apache Hadoop and HDFS: Learn about Apache Hadoop and the Hadoop Distributed File System.
2. Distributed Computing with Apache Spark: Perform distributed data processing with Apache Spark.
3. Data Streaming with Apache Kafka: Process and analyze streaming data with Apache Kafka.
4. Data Integration and ETL Tools: Explore popular data integration and ETL tools.

Group 9: Data Orchestration and Quality

1. Data Orchestration with Apache Airflow: Schedule and automate data workflows with Apache Airflow.
2. Data Quality and Validation: Ensure data quality and perform data validation.
3. Data Governance and Security: Understand data governance and security best practices.

Group 10: Cloud Data Engineering

1. Introduction to Cloud Computing: Learn the basics of cloud computing and its benefits.
2. Data Engineering in the Cloud: Explore data engineering concepts in cloud environments.
3. Data Warehousing in the Cloud: Build data warehousing solutions in the cloud.
4. Real-time Data Processing: Process streaming data in real-time using cloud services

…and the journey continues with detailed breakdowns of each group.

Photo by Sincerely Media on Unsplash

Learning Resources

To ensure a comprehensive learning experience, I’ve curated a collection of study materials and resources for each topic. You can find all the links in this handy guide:

Link to Study Material: https://github.com/DataShastra/100-Days_of_Python/blob/06c8c53716e2b989ea5b9c0c0092f26f665cf764/100%20Days%20of%20Python.csv

Why Share This Journey?

Sharing my journey is not just about my personal growth; it’s about building a community of passionate learners. I believe in the power of collaborative learning, and I want this journey to be an open space for knowledge sharing, discussions, and mutual support.

Join the Adventure!

Are you ready to dive into the world of Python and data engineering? Whether you’re a coding enthusiast, a curious learner, or simply someone who loves a challenge, I invite you to join me on this journey. Here’s how you can be a part of it:

1. Follow: Hit that follow button to stay updated on each day’s post.
2. Engage: Share your thoughts, ask questions, and discuss the topics in the comments.
3. Learn Together: Let’s create a community of learners who inspire and uplift each other.

Conclusion

The next 100 days are going to be a thrilling ride, filled with coding experiments, insights, and growth. So, gear up for a journey that will take your Python skills to new heights and introduce you to the exciting world of data engineering. Let’s make every day count, one line of code at a time! 🚀🐍

Thank you for joining me on this adventure! 🙌

#Python #PythonLearningJourney #DataEngineering #100DaysOfCode #DataShastra