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.
What you'll learn
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.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 1m
- Python for Data Analytics 6m
- Python Development Environments 5m
- Python Packages 4m
- Demo: Windows - Installing Python and Using Pip to Install Packages 4m
- Demo: MacOS - Using Brew to Install Python 3 4m
- Demo: MacOS - Using Pip to Install Packages 4m
- Demo: Installing and Working with Virtual Environments 3m
- Demo: Editing a Python Script Using Nano and Vim 3m
- Demo: Editing a Python Script Using SublimeText 3m
- Demo: Using Online Editors to Write Python Code 4m
- Module Summary 2m
- Module Overview 1m
- Introducing Jupyter Notebooks 4m
- Demo: Windows Installing Anaconda and Running Jupyter Notebooks 3m
- Demo: Mac OS Installing Anaconda and Running Jupyter Notebooks 3m
- Demo: Installing the Python 2 Kernel along with Python 3 3m
- Demo: Executing Code in Jupyter 4m
- Demo: Restarting and Switching Kernels 4m
- Demo: Exploring Magic Commands 5m
- Demo: Line Magic and Cell Magic Commands 4m
- Demo: Exploring Interactive Widgets 4m
- Demo: Wrangling and Visualizing Data 5m
- Module Summary 1m
- Module Overview 2m
- Exploring Popular IDEs for Python 3m
- Demo: Installing and Setting up IDLE 5m
- Demo: Running and Debugging Code with IDLE 6m
- Demo: Installing Eclipse and Setting up the PyDev Plugin 4m
- Demo: Running and Debugging Code with Eclipse 6m
- Demo: Installing and Setting up PyCharm 4m
- Demo: Running and Debugging Code with PyCharm 6m
- Demo: Working with Spyder 6m
- Module Summary 2m
- Module Overview 2m
- Jupyter on the Cloud 4m
- Demo: Getting Started with Azure Notebooks 4m
- Demo: Analyzing and Visualizing Data on Azure Notebooks 5m
- Demo: Setting up and Connecting to Cloud Datalab on the GCP 6m
- Demo: Building a Simple Regression Model on Datalab 6m
- Demo: Setting up a SageMaker Notebook Instance on AWS 4m
- Demo: Executing Code to Integrate with S3 Buckets 5m
- Summary and Further Study 2m
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
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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providin... more
