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Video: .MP4, 1280x720, 30 fps | Audio: AAC, 48 kHz, 2ch | Duration: 2h 22m
Genre: eLearning | Language: English | Size: 812 MB
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 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.
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