Building Your First Python Analytics Solution
By Janani Ravi
Course info
Course info
Description
Python has exploded in popularity in recent years, largely because it makes analyzing and working with data so incredibly simple. Despite its great success as a prototyping tool, Python is still relatively unproven for large, enterprise-scale development.
In this course, Building your First Python Analytics Solution you will gain the ability to identify and use the right development and execution environment for your enterprise.
First, you will learn how Jupyter notebooks, despite their immense popularity, are not quite as robust as fully-fledged Integrated Development Environments, or IDEs. Next, you will discover how different execution environments offer alternative ways of configuring Python libraries, and specifically how the two most popular, Conda and Pip, stack up against each other.
You will also explore several different development environments including IDLE, PyCharm, Eclipse, and Spyder.
Finally, you will round out your knowledge by running Python on the major cloud environments, including AWS, Microsoft Azure, and the GCP.
When you’re finished with this course, you will have the skills and knowledge to identify the correct development and execution environments for Python in your organizational context.
Course FAQ
Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux. It was created for Python programs, but it can package and distribute software for any language. Conda as a package manager helps you find and install packages.
Pip is a package-management system written in Python used to install and manage software packages. It connects to an online repository of public and paid-for private packages, called the Python Package Index.
An integrated development environment is a software application that provides comprehensive facilities to computer programmers for software development. An IDE normally consists of at least a source code editor, build automation tools and a debugger.
Python code needs to be written, executed and tested to build applications. The text editor provides a way to write the code. The interpreter allows it to be executed.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.
About the author
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
More from the author
Section Introduction Transcripts
Course Overview
Hi. My name is Janani Ravi, and welcome to this course on Building your First Python Analytics Solution. A little about myself. I have a Masters degree in electrical engineering from Stanford and have worked at companies, such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real time collaborative editing in Google Docs, and I hold four patents for the timeline technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. Python has exploded in popularity in recent years largely because it makes analyzing and working with data so incredibly simple. Despite its great success as a prototyping tool, Python is still relatively unproven for large enterprise scale development. In this course, you will gain the ability to identify and use the right development and execution environment for your enterprise. First, you'll learn how Jupyter notebooks, despite their immense popularity, are not quite as robust as fully-fledged integrated development environments, or IDEs. Next, you will discover how different execution environments offer alternative ways of configuring Python libraries, and specifically, how the two most popular conda and pip stack up against each other. You will also explore several different development environments including IDLE, PyCharm, Eclipse, and Spyder. Finally, you'll round out your knowledge by running Python on the major cloud environments, including AWS, Microsoft Azure, and the GCP. When you're finished with this course, you will have the skills and knowledge to identify the correct development and execution environments for Python in your organizational context.