1243 results for "python"

To start learning Python with a free, beginner-level course, check out Learn to Program: The Fundamentals from the University of Toronto, Problem Solving, Python Programming, and Video Games from the University of Alberta, Foundations of Data Science: K-Means Clustering in Python from the University of London, or Data Processing Using Python from Nanjing University. If you have some Python experience, you may also be interested in the intermediate-level free course, Python and Statistics for Financial Analysis from The Hong Kong University of Science and Technology.

If you are new to Coursera, you can also register today and start exploring our library of Python courses during your free seven-day, all-access trial. ‎

For Python beginners, we recommend starting with courses including Crash Course on Python from Google, Python for Data Science, AI & Development from IBM, or Learn to Program: The Fundamentals from the University of Toronto. If you may want to complete a beginner-level Specialization, we suggest starting with the first course in either the Python for Everybody Specialization or Python 3 Programming Specialization, both from the University of Michigan.

Each of these courses cover the basic principles of Python, such as variables and conditionals, functions, classes, and object-oriented programming, and can offer a great foundation when you’re getting started with Python. ‎

For people with advanced Python skills, we recommend Trees and Graphs: Basics from the University of Colorado, Boulder, part of the advanced Data Science Foundations: Data Structures and Algorithms Specialization. Here are a couple more advanced Python courses we recommend based on specific learning goals:

• For a deeper dive into machine learning, check out IBM’s Advanced Machine Learning and Signal Processing, part of the Advanced Data Science with IBM Specialization.

• To get into web development, try Codio’s Advanced Django: Introduction to Django Rest Framework, part of the Advanced Django: Mastering Django and Django Rest Framework Specialization.
• To learn more about MLOps and ML platform engineering, see Duke University’s Open Source Platforms for MLOps.
• To learn about developing custom data pipelines, look at Google Cloud’s Serverless Data Processing with Dataflow: Develop Pipelines, part of the Serverless Data Processing with Dataflow Specialization.‎

Python is among the most popular programming languages due to its versatility and simple, English-based language structure. With its widespread use, it tends to be an in-demand skill in several fields, including data analysis, software engineering, and IT.

Although you don’t need a Python certification in order to obtain roles in these fields, certificates can be a valuable credential that signifies your expertise to potential employers. Additionally, through the coursework you’ll complete while pursuing a certificate, you’ll be able to further develop your skills and confidence. You may also complete projects which you can then incorporate into your portfolio to further demonstrate your knowledge and creativity. ‎

Python is commonly used in roles relating to data analysis, software engineering, and web development. This general-purpose programming language can be used for a range of tasks, including statistical analysis, data visualization, automation, and machine learning. Some job titles for people that may use Python include data analyst, data scientist, data engineer, quality assurance engineer, Python software engineer, back-end developer, full stack developer, or Python developer.‎

Although it can be helpful to have some experience working with any programming language, you don’t need any previous programming experience before getting started with Python. In fact, Python is typically one of the first languages programmers learn because of its simplicity and versatility. At the beginner level, you can learn the fundamentals of Python in a matter of months with programs like the Python for Everybody Specialization from the University of Michigan.

Read more tips for learning Python.‎

Both Python and R are free, open-source languages that can run on Windows, macOS, and Linux. Python can be used for a range of tasks, but is commonly used for data science and data analysis, web application development, and automation or scripting. It tends to be better for handling massive amounts of data, building deep learning models, and performing non-statistical tasks such as web scraping and running workflows.

R is a statistical programming language and is commonly used for manipulating data, statistical analysis, and data visualization. It tends to be better for creating graphics, building statistical models, and utilizing its robust ecosystem of statistical packages.

Learn more about whether Python or R is right for you.‎

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