Python Roadmap from Scratch

Basics

Steps:

1. Native Data Structures
2. Basic Syntax
3. Logic Statements
4. Loops
5. Start Solving Problems
6. Search for better solutions
7. Learn OOPs in parallel

Outcome:

1. Overiew of the language
2. Knowledge of common modules and function
3. Will be able to read other's code

Resources:

1. W3Schools Python Tutorial
2. Python Courses
3. Practice at Hackerrank
4. Articles at GeeksforGeeks
5. Corey Schafer YouTube Videos

Project Ideas

1. Automating Excel Tasks
2. Web Page Scraping
3. More Project Ideas At

Intermediate

Steps:

1. Apply OOPs concepts in your code
2. Start Following Pylint standards
3. Learn Data Structures and Algorithms
4. Start Exploring other modules (pandas, requests etc)
5. Start controlling versions using Git
6. Create end to end projects
7. Learn about testing
8. Use multi-processing, multi-threading in projects

Outcome:

1. Should have a decent understanding of the language
2. Sould be identifying problems and solving them using Python
3. Should be able to understand third party module and their documentation
4. Should be able to create tests for the projects that you create
5. Should be able to speed up codes

Resources:

1. Corey Schafer OOPs tutorial
2. Practice Problem Solving at Hackerrrank
3. Practice DSA at Hackerrank

Project Ideas

1. Create an ETL pipeline
2. Create a package and deploy in github
3. Create API using FastAPI

Advance / Niche

1. Web and Backend Development

1. Learn Django, Flask, FastAPI
2. Basics of HTML, CSS, JS
3. Authentication and Authorisations
4. Rest APIs and framework
5. Synchronous and Asynchronous architechture
6. Create a website
7. Create APIs
8. Deploy in Cloud Platforms (e.g Heroku, AWS etc)

2. App development

1. Learn about specialised modules (tkinter, pyGame, PyQT, Kivy)
2. Create simple project using them
3. Deploy and actively maintain them

3. Data Science / Analytics / Data Engineering

1. Learn about APIs, Web scraping, Cleaning
2. Learn about ETL Pipelines and tools like Airflow
3. Know about parallel processing frameworks like Dask and PySpark
4. Basics about Big Data
5. Learn about specialised libraries like (Pandas, Dask, Sklearn, PyTorch, Tensorflow, Keras)
6. Do hands on ML projects and deploy end results

Others:

1. Basics of DevOps
2. Knowledge about Docker and Kubernetes

Outcome:

1. Should be able to solve problems and build end to end projects proficiently.
2. Should have enough projects in hand to showcase.
3. Should be able to start applying for internships and full time jobs.

Resources:

1. Web developement
2. App Development
3. Data Science / Analytics / Data Engineering Courses
4. DevOps Courses

Walkthrough