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Data is at the core of Data Science. Statistics is all about understanding data in a mathematical way for actionable insights. In this tutorial we talk about what when & why of statistics, as well as different stages through which any statistical analysis project will go through. 

The different stages are as follows:

 - Business Objective 

- Data Collection 

- Data Visualization 

- Data Pre Processing 

- Modelling 

Top Python Libraries | Data Processing & Visualization | Python Training | Python Tutorial | ExcelR

In this video, you will learn about how to install anaconda navigator, launching Jupyter, a brief introduction to various Python libraries that includes NumPy, panda, sci-kit-learn, Seaborn, etc. You will also learn more about panda libraries, like adding data structure, tools to work with table-like data, and data modeling that includes - sorting, reshaping, merging, slicing, and aggregation. The advantage of using python is handling missing data. This program is an initiation for engineering students in partnership with state and government bodies, where they will not only learn from the basics to the advanced level but also a complete practical oriented hands-on training studded with assignments and projects. Enrolled students will work parallelly through assignments by learning industrial case studies, data analysis, and part of machine learning algorithms and various industrial applications.

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Text  Preprocessing Using Python | ExcelR This video goes through an end-to-end Text Pre-Processing Using Python 1.NTLK 

2.Stemming 

 3.Cleaning 

4.Stop Words 

5.Corpus

 6.Lemmatization

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