At HackerCulture , we're all about Python. Our mission is to get more people familiar with the Python programming language, and we teach you how to use python to write better programs.

Python is the most loved programming language by both beginners and experts, according to multiple surveys. And not only software developers but other people from different disciplines use Python for a variety of different tasks, such as data analysis and visualization, artificial intelligence and machine learning, automation, etc.

There has never been a better time to learn Python, and with our resources created by professional Python developers with decades of experience, you can learn the skills and techniques that really matter in the real world. You can use Python to build web apps, mobile apps, and desktop applications, as well as software testing and even some actual hacking.

Whether your goal is to learn python for web development, data science, or machine learning, we'll go over the most important libraries and resources to learn after getting the basics down.

Table of Contents

• Python Basics
• Intermediate Python
• Advanced Python Topics
• Python for Web Development
• Python for Automation
• Python for DevOps
• Python for Data Science
• Python for Machine Learning
• Tips for learning Python programming

Python Basics

First, we need to install python so we can play around with it:

Setting Up Python and Virtualenvs with Pyenv

Then, you can follow this roadmap to go from Python beginner to knowing "just enough to be dangerous" with Python. While this list doesn't cover every single feature, it does cover many of Python’s most noteworthy features and will enable you to read and write Python modules and programs.

1. Python Basics: Code Your First Python Program
2. Variables and operators
3. Numbers and math
4. Strings and string methods
5. Basic Data Types in Python
6. Loops in Python
7. Exception handling
8. Functions
9. Modules

Intermediate Python

This section will give you the tools to make complex applications and introduce you to concepts about Object Oriented Programming (OOP) in Python:

1. Classes and Objects
2. File System Operations
3. Dictionaries
4. Lists and List Comprehensions
5. String Formatting
6. Common Data Structures in Python

Advanced Python Topics

This section builds upon the previous ones and teaches you how to use frameworks & libraries available in the Python language:

1. Asyncio
2. Multithreading
3. Multiprocessing

At this point in time, once you have gone through all the previous steps, you should have everything you need to make use of Python in real-world applications.

Python is used in almost every industry, and you have a few career path options. Let's look at some of these.

Python for Web Development

The role of a web developer is to build websites and applications that are accessible using a web browser. Python is typically used on the backend side.

A popular web framework that many companies use to build web applications is Flask. This tutorial will help you become familiar with building apps with Flask:

Python REST APIs with Flask

You should also become familiar with databases. This tutorial covers how to integrate databases with your Flask server:

Connecting a Database to your Flask Project with SQLAlchemy

You can also learn how to deploy Flask apps so you can share them with the world in this article:

How to deploy your Flask apps

Finally, you may be interested in learning more about the web and how your users will interact with your apps on their browsers. This course will help you become more of a well-rounded full-stack web developer:

Web Design Course for Everybody

Python for Automation

Automation is the field of performing tasks that are repetitive or time-consuming, such as data entry, web scraping, and automation of various system processes, in a way that they can be reused.

A specific use case for automation is the continuous testing of software projects, in which we write scripts to interact with an existing program and report errors.

Getting started with testing in Python

Automation can also be used to control other software, such as a web browser, Excel, or even hardware, such as robotic systems. Some libraries worth checking out for this are:

• Selenium
• Beautiful Soup

Python for DevOps

DevOps engineers are responsible for implementing and maintaining infrastructure to support software systems, and they work to ensure that software systems are deployed quickly, efficiently, and with minimal downtime.

This book provides a comprehensive introduction to the field of DevOps:

Python for DevOps: Learn Ruthlessly Effective Automation

Some of the other technologies used by DevOps engineers to automate software deployments and maintain a smooth operation are:

• Docker
• Kubernetes

Python for Data Science

A data scientist's primary concern is to extract insights and knowledge from data. This typically involves a number of different tasks, including collecting and cleaning data, developing predictions and forecasts using statistics, and visualizing results.

A data scientist must be familiar with databases and able to query large amounts of data in order to identify patterns and trends. Usually, the main tools used by data scientists are:

1. Free, Interactive SQL Course
2. Pandas
3. Data Visualization

If you want to get deeper into data science, consider the following books:

Data Science from Scratch

Python for Data Analysis: Data Wrangling with Pandas, NumPy, IPython

Python for Machine Learning

A machine learning engineer creates and develops computer programs that can "learn" from data and make predictions or decisions without being explicitly programmed to do so to solve real-world problems like predicting the weather or detecting facial expressions.

This typically involves working with large sets of data, which can be explored with the pandas library to prepare the data for use in models:

Getting started with Pandas in Python

A very popular library that helps with training machine learning models for different problems is Scikit-learn.

If you are interested in building other language applications like translating speech to text or summarizing a long article, these resources are for you:

Natural Language Processing (NLP) with Python - Tutorial

If you prefer to read a book instead, these books are an excellent way to get into machine learning with Python:

Introduction to Machine Learning with Python

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Tips for learning Python programming

Learning how to write computer programs is hard, but learning Python, in particular, is significantly easier than many other programming languages. These tips will help you stay focused and motivated to achieve your goals:

• Have patience. Learning a new language always requires more effort and time, so understand that it will take time to let everything sink in.
• Don't feel intimidated by complex terms, errors, and issues. Don't give up, this happens to everyone in programming. Research the topic using other resources and come back to it with a clear mind.
• Frustration and pain are a part of the process. Embrace them instead of avoiding them, and you will conquer them.
• Be consistent. If you are not consistent in learning, it will take a lot more time and effort.
• Don't try to learn everything at once. It's better to practice a specific topic every day before moving on to the next.
• Build small projects to try out the things you learn and build up your confidence. You should add them to your github profile to help you land jobs.

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