Building Your First Python Analytics Solution

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Getting Started with Python for Analytics

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Python for Data Analytics

05. Python Development Environments

06. Python Packages

07. Demo- Windows - Installing Python and Using Pip to Install Packages

08. Demo- MacOS - Using Brew to Install Python 3

09. Demo- MacOS - Using Pip to Install Packages

10. Demo- Installing and Working with Virtual Environments

11. Demo- Editing a Python Script Using Nano and Vim

12. Demo- Editing a Python Script Using SublimeText

13. Demo- Using Online Editors to Write Python Code

14. Module Summary

3. Working with Python Using Anaconda

01. Module Overview

02. Introducing Jupyter Notebooks

03. Demo- Windows Installing Anaconda and Running Jupyter Notebooks

04. Demo- Mac OS Installing Anaconda and Running Jupyter Notebooks

05. Demo- Installing the Python 2 Kernel along with Python 3

06. Demo- Executing Code in Jupyter

07. Demo- Restarting and Switching Kernels

08. Demo- Exploring Magic Commands

09. Demo- Line Magic and Cell Magic Commands

10. Demo- Exploring Interactive Widgets

11. Demo- Wrangling and Visualizing Data

12. Module Summary

4. Working with Python Using Other IDEs

01. Module Overview

02. Exploring Popular IDEs for Python

03. Demo- Installing and Setting up IDLE

04. Demo- Running and Debugging Code with IDLE

05. Demo- Installing Eclipse and Setting up the PyDev Plugin

06. Demo- Running and Debugging Code with Eclipse

07. Demo- Installing and Setting up PyCharm

08. Demo- Running and Debugging Code with PyCharm

09. Demo- Working with Spyder

10. Module Summary

5. Working with Python on the Cloud

1. Module Overview

2. Jupyter on the Cloud

3. Demo- Getting Started with Azure Notebooks

4. Demo- Analyzing and Visualizing Data on Azure Notebooks

5. Demo- Setting up and Connecting to Cloud Datalab on the GCP

6. Demo- Building a Simple Regression Model on Datalab

7. Demo- Setting up a SageMaker Notebook Instance on AWS

8. Demo- Executing Code to Integrate with S3 Buckets

9. Summary and Further Study

439,000 تومان

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تولید کننده: PluralSight