- Webpage (preferably hosted on github.io)
- High quality resume (typed in latex)
- LinkedIn profile (take skill tests, get recommendations)
- GitHub (command line)
- Linux commands (top 25)
- Bash scripting (basics)
- VSCode
- Working with remote computers/Docker via SSH (VSCode is best for this)
- Virtual envs (conda, pipenv, venv, virtualenv)
-
Web scraping & automation
- requests, requests_html, httpx (For HTTP requests. Learn to Develop & hack APIs)
- BeautifulSoup (for parsing html/text)
- Selelnium (most powerful automation tool, must learn. People use it to build twitter bots to testing products at google/meta)
- scrapy (popular scraping framework)
-
Testing
- unittest, doctest, pytest (testing units of code)
- locust (Scalable user load testing tool)
-
API Devlopment
- FastAPI (FastAPI framework, high performance, easy to learn, fast to code, ready for production)
- Flask & flask-restful (micro framework for building web applications)
- django, django-rest-framework(python backend and API devlopment), celery (Distributed Task Queue)
- API Testing
- httpie (test APIs in terminal / Web)
- Postman
-
Visualization
- bokeh (Interactive Data Visualization in the browser)
- Plotly (Interactive graphing library for Python)
- matplotlib (Plotting for Python)
- altair (Declarative statistical visualization library for Python)
- Redash (Connect to any data source, easily visualize, dashboard and share your data)
-
Data
- pandas (powerful data analysis / manipulation, data.frame objects, statistical functions, and much more)
-
Matrix
- numpy (Fundamental package for scientific computing)
-
Deployments
- Streamlit (The fastest way to build data apps in Python)
-
Machine Learning & Deep Learning
- sckikit-learn (machine learning in Python)
- Keras ( Deep Learning for humans)
- PyTorch (Tensors and Dynamic neural networks in Python with strong GPU acceleration)
- Tensorflow (Open Source Machine Learning Framework)
-
Others simple yet powerful (must use)
- attrs (Python Classes Without Boilerplate)
- asyncio
- re — Regular expression operations
- math — Mathematical functions
- random — Generate pseudo-random numbers
- statistics — Mathematical statistics functions
- itertools — Functions creating iterators for efficient looping
- glob — Unix style pathname pattern expansion
- shutil — High-level file operations
- pathlib — Object-oriented filesystem paths
- os — Miscellaneous operating system interfaces
- io — Core tools for working with streams
- time — Time access and conversions
- argparse — Parser for command-line options, arguments and sub-commands
- logging — Logging facility for Python
- threading — Thread-based parallelism
- multiprocessing — Process-based parallelism
- concurrent.futures — Launching parallel tasks
- json — JSON encoder and decoder
- typing — Support for type hints
- Python
- SQL(PostgreSQL)
- Writing good markdown readme for projects/repos