Why learn Python
for free with Flatiron School?
Explore your future in tech
Our introductory lessons to help you learn Python basics are the best way to experience if learning to code is for you. Explore whether you’re passionate about pursuing data analysis and coding as a career, or if you just want to brush up on some skills.
Prepare for a data science bootcamp
These lessons are an efficient way to get real hands-on experience and learn the Python basics you’ll need to get into our immersive data science course.
What you’ll learn: Python basics
In this Python for Beginners tutorial, you’ll explore how Python can help organize a data set of travel destinations, helping you access different components using different Python skills and techniques.
Learn Python Data Types
Learn how to identify string and float data types, integers, booleans, and type() methods.
Understand the ‘list’ Data Type
Access elements of a list in Python and assign a single element of a list to a variable.
Assign a List to a Variable
Dive into the traits of a list and see how to access different elements and components of Python lists.
Use the Index of the Items in a List
Learn how Python assigns numbers to each element in a list using the index method.
Other free data science tutorials at Flatiron School
Intro to Word Clouds & Python
Learn how to analyze famous song lyrics using word clouds, Python, and data visualization.
Intro to Binomial Distribution
In this intermediate workshop, use Python to demonstrate how data can optimize hotel profits.
Learn to Code Python: Free Lesson for Beginners
How to Get Hired in Data Science without a Master’s Degree
Job postings for your dream jobs in data science all seem to require a master’s degree — is there any way to get into data science without getting a master’s? The answer is simple: absolutely. And we have plenty of proof to back that up.
Python is a general-purpose coding language. This means that, unlike some other languages, it can be used for other types of programming than web development alone. Python can be used to manipulate and manage big data sets, and is known for being able to perform complex mathematical computations, which makes Python the most important programming language in data science.
Python is a general-purpose programming language with a broad range, meaning that it can be used for things like software development, writing system scripts, and mobile app development. Python can also be used to read data files and analyze data sets, making it an attractive language in data science.
Further reading: The Best Programming Languages to Learn for Data Science
Compared to some other language, Python is pretty easy to learn, but the answer to this question really depends on what you want to achieve. You can learn some basics in Python in just a few hours, but to really start getting comfortable with Python probably takes a few weeks of intense training.
It is also important to know that even people with years and years of experience don’t know everything about Python and its abilities because Python is a language that keeps evolving.
Python is best used for automation, back-end web and app development, software development, processing big data, system scripts.
Automating tasks is extremely valuable in data science and will ultimately save you a lot of time, and provide valuable data — this is why Python excels.
While Python supports all the basic features of an Object-Oriented Programming Language, Python isn’t strictly an OOP language and has features of aspect-oriented programming as well.
Yes, Learn Python is free, just like the rest of our workshops and tutorials.
If you’re looking to get started with Python for free, you’re in the right place. This Python tutorial for beginners covers Python basics entirely for free.
You should learn Python if you’re interested in writing scripts, app or software development, and data science. If you’re interested in learning Python to analyze data specifically, it’s both a very useful language for those who want to fully change careers and become data scientists and analysts, and those who want to use data analysis in their current job, but want to leverage data skills to push their careers further.