Scientific Python : Learn to Analyze Text Data using Python and Pandas

Scientific Python (Pandas)

The ranking of universities has become a common task performed by many institutions, each of them proposes a different ranking based in several weighted categories. Examples of those rankings are: [Webometrics Ranking of World Universities](http://www.webometrics.info/), [THES - QS World Universities Rankings](http://www.thes.co.uk/worldrankings) and [Academic Ranking of World Universities](http://www.arwu.org/ranking.htm). The first ranking measures the visibility of the universities and their global performance in the web. The last two attempt to measure the performance of the universities based in categories like prizes received by members, citations, and publications. Employers, especially from the multinational organisations use rankings to find universities to source graduates, so attending a high-ranking university can help in a competitive job market.

In this lesson we will use a simple (publicly available) dataset obtained from the [data.world](https://data.world/education/university-rankings-2017) called: **US News Universities Rankings 2017 edition**. From this data, using **Python and Pandas** we will explore different features and finally find an interesting fact about the correlation of tuition fees and uni rank. In each project described in this course, we will attempt to learn a few Python commands and tricks. 

 ## Learning objectives
By completing this, you will learn to use the following  Pyhton and Pandas function:


 + `Pandas dataframes` -- output the first part of files
 + `Python Regex` -- opposite to head
 + `Python print` --  concatenate and print files
 

## Data download
You should download the data from [here](http://educative.io/udata/4Wyl0VOx0b0/unirank.csv) , as we have slightly simplified the data and let's save the data as: `unirank.csv`. I believe my course would be incomplete without video demos of the commands I am showing here. Therefore, I will add a video demo with each lesson. Watch and enjoy it before you proceed to the next part:


The ranking of universities has become a common task performed by many institutions, each of them proposes a different ranking based in several weighted categories. Examples of those rankings are: [Webometrics Ranking of World Universities](http://www.webometrics.info/), [THES - QS World Universities Rankings](http://www.thes.co.uk/worldrankings) and [Academic Ranking of World Universities](http://www.arwu.org/ranking.htm). The first ranking measures the visibility of the universities and their global performance in the web. The last two attempt to measure the performance of the universities based in categories like prizes received by members, citations, and publications. Employers, especially from the multinational organisations use rankings to find universities to source graduates, so attending a high-ranking university can help in a competitive job market.

In this lesson we will use a simple (publicly available) dataset obtained from the [data.world](https://data.world/education/university-rankings-2017) called: **US News Universities Rankings 2017 edition**. From this data, using **Python and Pandas** we will explore different features and finally find an interesting fact about the correlation of tuition fees and uni rank. In each project described in this course, we will attempt to learn a few Python commands and tricks. 

 ## Learning objectives
By completing this, you will learn to use the following  Pyhton and Pandas function:


 + `Pandas dataframes` -- output the first part of files
 + `Python Regex` -- opposite to head
 + `Python print` --  concatenate and print files
 

# Data download
You should download the data from [here](http://educative.io/udata/4Wyl0VOx0b0/unirank.csv) , as we have slightly simplified the data and let's save the data as: `unirank.csv`. I believe my course would be incomplete without video demos of the commands I am showing here. Therefore, I will add a video demo with each lesson. Watch and enjoy it before you proceed to the next part:


Introduction

Basics

Pandas Series

Project 1: Analysing the 'US News' university ranking data

Introduction