Building Your First Python Analytics Solution

This course covers the important aspects of choosing a development environment for Python, the differences between Conda and Pip for working with Python libraries, popular IDEs such as  PyCharm, IDLE, Eclipse, and Spyder, as well as running Python on the cloud.
Course info
Rating
(41)
Level
Beginner
Updated
Oct 29, 2019
Duration
2h 46m
Table of contents
Course Overview
Getting Started with Python for Analytics
Working with Python Using Anaconda
Working with Python Using Other IDEs
Working with Python on the Cloud
Description
Course info
Rating
(41)
Level
Beginner
Updated
Oct 29, 2019
Duration
2h 46m
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.

About the author
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
Getting Started with Tensorflow 2.0
Beginner
3h 9m
Jul 23, 2020
More courses by Janani Ravi
Section Introduction Transcripts
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.