Learn Python for Data Analysis and Visualization

When it comes to data analysis and manipulation the Python Pandas library is one of the most used libraries in Python. Whether in finance, scientific fields, or data science, a familiarity with Python Pandas is a must have.

This course teaches you how to work with real-world data sets for analyzing data in Python. Not only will you learn how to manipulate and analyze data you will also learn powerful and easy to use visualization techniques for representing your data. 

By the end of this course you will know how to:

• Use Anaconda, the worlds leading data science platform, to setup Python and manage libraries

• Install and setup the free to use Atom Text Editor

• Create Virtual Environments

• Clone a GitHub Repository directly into Atom

• Create new code branches in GitHub and Atom

• Install the Pandas library

• Use Pandas DataFrames for data analysis

• Quickly and efficiently inspect large data files

• Use conditional filtering to refine your data

• Use NumPy and Pandas together

• Create DataFrames without starting data files

• Create DataFrames from dictionaries

• Use Broadcasting to create DataFrames

• Correctly label data within DataFrames

• Cleanse your data files for easier analysis

• Create graph plots from your data (line, bar, scatter, area and more)

• Save and export your data files for sharing

• Use statistical exploratory data analysis techniques such as min, max, mean on your data

• Mange date and time data within large data sets

• Create Date/Time indexes

• Partial string indexing

• Resampling techniques such as downsampling

• Method chaining

This course kicks off by showing you how to get up and running using GitHub, an essential skill in your coding career. Ideally, to get the best from this course you should have some Python programming experience.

Every piece of code and dataset used in this course is available to download for free from GitHub.

Without a doubt, this course will teach you the necessary skills to apply basic data science techniques which are used the world over by experienced data scientists and those who spend their working day in spreadsheets.