Video: .MP4, 1280×720, 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
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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