Company: Pluralsight Author: Xavier Morera Full Title: Programming Python Using An IDE Year: 2019 Language: English Genre: Educational: Programming Skill Level: Intermediate Price: - - Files: MP4 (+ Exercise Files, Slides .PDF) Time: 02:00:04 Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 250 kbps Audio: AAC at 96 Kbps, 2 channels, 48.0 KHz Once you have learned the foundations of Python, the next step to increase your productivity is learning how to be proficient with a development environment or IDE. This course will help you get started. Learning and becoming proficient with Python is one of the best decisions a coder can make. The simplicity of Python, along with the many libraries available make it one of the most productive languages you can use. This course, Programming Python Using an IDE, will help you use an IDE to take your coding skills one level higher! First, you will explore the selection of popular IDEs and how they can help you improve your productivity. Next, you will learn about the many features that make IDEs great for creating applications including syntax highlighting, refactoring, code checking, and more. You will also discover some other features that help you run, debug, unit test, and source control your code. Finally, you will see how some IDEs have features that are meant for scientific Python and creating data science applications. By the end of this course, you will know and understand how IDEs can help you be a more productive developer. Lessons: 1. Course Overview 01. Course Overview 2. Programming Python Using an IDE! But Why? And Which One? 02. Programming Python Using an IDE! But Why? And Which One? 03. Code Editors vs. Integrated Development Environments 04. Overview of Available Code Editors for Python 05. Configuring Python Features for Specific Code Editors 06. Takeaway 3. Improving Your Productivity Programming in Python with an IDE 07. Improving Your Productivity Programming in Python with an IDE 08. Customizing IDEs 09. Features That Improve Productivity Coding with an IDE 10. Organizing, Navigating, Refactoring, and Styling Code 11. Running and Debugging Python Code with an IDE 12. Integrating with Version Control Using an IDE 13. Working with Databases in Python Using an IDE 14. Unit Testing with an IDE 15. Takeaway 4. Leveraging a Python IDE for Data Science 16. Leveraging a Python IDE for Data Science 17. Leveraging Data Science and Scientific Tools in PyCharm 18. Working with an IDE Built for Scientific Python: Spyder 19. Using Jupyter Notebook for Data Science 20. Using Apache Zeppelin for Data Science 21. Cloudera Data Science Workbench for Data Science at Scale 22. Takeaway 5. Final Takeaway 23. Final Takeaway Our members see more. Join us! ------------- Our members see more. Join us!
