Statistical analysis github

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    Power BI Data Analyst and Power Platform Developers can use GitHub too.
    Yes, You can Leveraging GitHub for project documentation and showcasing is another way to get more value from your projects..

  • Is GitHub useful for data analysis?

    Data Scientists need GitHub for source code management.
    It hosts Git, an open-source version control system that tracks the changes and requests of a project..

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    Statistical Analysis Software
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    Over 90 percent of Fortune 100 companies use GitHub.
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  • Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data.
Practicum by Yandex Project 3: This Statistical Data Analysis project is prepared to analyze clients' behavior and determine which prepaid plan brings in more 
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Description

This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects.
Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis.
Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory.
This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data.
Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls.
Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas.
This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods.
Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

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Outline

Introduction to Pandas

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Overview

Introductory Tutorial, SciPy 2013, 25 June 2013

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Required Packages

•Python 2.7 or higher (including Python 3)


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