mrcreasey/oc-ds-p2-exploratory-data-analysis

Analysis of education indicators

Analysis of education indicators

Overview

The aim of this project is to identify countries with a strong potential for online e-learning for adults with a high level of education (at least a baccalaureat), through :

1. selection of suitable indicators from the world bank (explore at https://datatopics.worldbank.org/education/);
2. exploratory data analysis of these indicators

Motivation

This is project 2 for the Master in Data Science (in French, BAC+5) from OpenClassrooms.

The project demonstrates the inital steps in a data science project :

• configuring a python environment, and using a jupyter notebook
• importing, joining and describing the files in a dataset
• validating, cleaning and selecting relevant data
• elementary exploratory data analysis (distributions, correlations, temporal evolutions)

Requirements

The notebook includes a list of its own requirements, and a procedure for pip install of any missing libraries. It also contains procedures to download and unzip the data, if not already downloaded.

Data : The dataset of education indicators used in this analysis can be downloaded (37Mb) from the worldbank at https://databank.worldbank.org/data/download/Edstats_csv.zip.

Python libraries : numpy, pandas, matplotlib, seaborn, missingno, plotly, tqdm

Files

• P2_01_notebook.ipynb : import, cleaning, exploratory data analysis
• P2_02_support.pdf : a presentation of the project

Note : Files are in French. As requested by the jury, the notebook has not been cleaned up : the focus is on data manipulation and exploration

Approach

The net income for a company offering e-learning is assumed to depend on :

• The population selected for marketing (a country, a town, a field of study)
• The percentage of this population with access to e-learning
• The percentage of this population educated to at least baccalauréat
• The driving factors for e-learning (for example: unemployment, rurality, gender parity, availabilty of courses)
• The net price the students are willing and able to pay for e-learning

An indicator is selected for each of these factors.

The evolution of these indicators for different e-learning business models are explored :

• with / without expensive student accompanyment
• with / without reconversion of the workforce (academic vs professional courses)

Based on the selected indicators, the countries with the highest potential are found to depend strongly on the business model

Keywords

• python, data-preprocessing, exploratory data analysis, data visualisation
• correlation heatmap, pairplot, barplot, lineplot, jointplot, chloropleth
• business model, performance indicator

Skills acquired

• Set up a Python environment
• Use a Jupyter notebook to facilitate code writing and collaboration
• Manipulate data with specialized Python libraries
• Master the fundamental operations of the Python language for Data Science
• Perform a graphical representation using a suitable Python library