Learn about the most common mistakes that emerging data scientists make while using Python, as well as how to avoid these missteps in your own work.

Visually study the relationship of data structures and algorithms. Learn how stacks, queues, and 2D lists are used with depth-first, breadth-first, and A-star search algorithms.

Rock your next technical interview by learning how to communicate your understanding of linked lists.

Rock your next technical interview. Learn about the top three linear data structures—stacks, queues, and deque—and build your own data structures in Python.

Learn about binary search trees in Python and how to create, navigate, modify, and use them in a real-world context.

Learn how to use dictionaries to store and retrieve unordered data in Python.

In this course, instructor Mridu Bhatnagar takes you through the basics of using Python sets and frozen sets. If you’re in the early stages of your Python programming career, or if you just need a refresher, join Mridu as she discusses the basic mechanics and functions of these data structures. Mridu gives a high-level overview of sets and frozen sets and then dives into the various methods to work with them. She discusses use cases and topics like how to modify a set and use built-in functions. Mridu also provides challenge and solution videos to test your knowledge as you go along. If you’re looking to learn important aspects about sets and frozen sets in Python, without jumping into a long project-based course, Mridu’s course is for you.

Learn how to use four essential libraries that every Python developer should consider for common development scenarios: Pendulum, Requests, PyFilesystem, and Pillow.

Review the syntax of this simple programming language, and learn how to write scripts, create modules, and integrate a database with Python 3 and the Python database API.

Learn Python programming for data science. Discover how to clean, transform, analyze, and visualize data, as you build a practical project: an automated web scraper.

Learn Python programming for data science. Part 2 describes how to use machine learning to generate predictions and recommendations and automate routine tasks.

Learn 12 techniques for how to work with data using Python. Review how to explore data, ingest data, visualize data, and more.

Build accurate, engaging, and easy-to-generate data visualizations using the popular programming language Python.

This course offers scientists and engineers (ranging from students of those disciplines to experienced professionals) a dedicated, empowering introduction to Python for scientific and engineering applications. Theoretical astrophysicist and Python enthusiast Michele Vallisneri explains how Python can help you become a better engineer or physicist by making your work more efficient, accurate, and agile. Michele walks you through installing Python for macOS, Windows, and Linux, as well as setting up Jupyter notebooks. He explains how you can make Python fast using NumPy arrays, the SciPy library, Numba, and Cython. Michele then tackles ways to ensure your code is correct with tools for symbolic computation, differential equations, interpolation, and more. He finishes up with ideas to make your computational life easier with Python, including JSON, pandas, HDF5, automation with Python scripts, and scientific workflows with Snakemake.

Do you need to learn Python for your current job, for the job you want, or just for fun? In this course, instructor Ronnie Sheer shows you how you can leverage your existing knowledge of JavaScript to get started programming in Python. Ronnie compares Python and JavaScript, then covers how to set up and run a Python environment. He discusses how to write code that adheres to Python conventions, which can prevent bugs. Ronnie explains some of the basic logic in Python, including the use of indentation, how to write and debug control flow, how to use Python try/except appropriately, and more. He goes over objects and class definitions, then explores Python’s data model feature and mixins class. Ronnie goes into iteration in Python and shows how you can write more concise and elegant code in Python by using zip, enumerate, and list comprehensions. He concludes with a challenge that shows you how to apply your knowledge of JavaScript and Python to improve a small program.

Learn programming with Python in this fun and easy-to-follow course designed for high-school and college-age students.

Expanding your C# knowledge to include Python gives you a competitive advantage in the job marketplace. In this course, Joe Marini helps you translate your knowledge into the Python language. Python and C# share many similarities but are different in important ways. Joe explains the difference between C# and Python comment syntax. Python’s conditional statements and loop control statements are somewhat limited compared to C#. Joe walks you through the differences. Functions are a basic part of most programming languages. Joe shows you how several important functions work in Python. Python supports class definitions, single and multiple inheritance, abstract classes, and custom functionality. Joe compares and contrasts these with C# functionality. Joe shows you how to build and work with Python arrays of various data types, how to declare and use dictionaries in Python, and how to use iterators in Python. In conclusion, Joe discusses common operations like strings and exceptions.

Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.

Learn how to build a graphical user interface (GUI) for your Python applications with Tkinter.

Learn how to automate the configuration of network devices with NAPALM, a Python library used to automate and interact with network devices and OSs using a unified API.