Course Outline

Introduction

  • Overview of Python and its Powerful Ecosystem for Data Analysis

Getting Started

  • Setting up the development environment
  • Installing Python, Numpy, and Pandas
  • Installing Jupyter

Python Programming for Data Analysis

  • Overview of Python syntax
  • Writing and running Python code

Working with Data

  • Importing a dataset
  • Cleaning the data

The Python Data Frame

  • Understanding data frames
  • Manipulating data in a date frame

Gaining Insights from Data

  • Summarizing the data
  • Generating reports
  • Visualizing data

Saving Your Python Code

  • Saving your code in a version control repository
  • Allowing others to access your code

Improving Your Code

  • Testing your code and fixing the errors
  • Tightening your code using an iterative approach

Taking Your Code to Production

  • Uploading your code to a website
  • Automating the executing of your code

Python Programming Best Practices

Summary and Conclusion

Requirements

  • Programming experience in any language

Audience

  • Developers
  • Beginning data scientists
  • Business analysis with technical skills
  28 Hours
 

Testimonials

Related Courses

SPSS Modeler

  14 hours

Databricks

  14 hours

Microsoft Power Platform Fundamentals

  14 hours

PL-900T00: Microsoft Power Platform Fundamentals

  7 hours

Data Cleaning

  7 hours

Sensu: Beginner to Advanced

  14 hours

Monitoring Your Resources with Munin

  7 hours

Automated Monitoring with Zabbix

  14 hours

Fluentd for Log Data Unification

  14 hours

Nagios Core

  21 hours

Nagios

  35 hours

Nagios XI Administration

  21 hours

Advanced Nagios

  21 hours

Zenoss Monitoring for Administrators

  21 hours

Netdata

  7 hours