Investigate Children Out of School
Introduction
Gapminder has collected a lot of information about how people live their lives in different countries, tracked across the years, and on a number of different indicators. To create a project, at least three indicators should be selected to investigate.
(Image is based on a free chart from www.gapminder.org.)
- Data is provided as Excel spreadsheet files. We can use a spreadsheet program to export each table as a csv file;
- Firstly, we want to look into ways of reshaping the data so that it is tidy, especially if we want to do comparisons across indicators. After joining the data together, the columns might look like: {Country, Year, Indicator 1 Value, Indicator 2 Value, … };
- Some of the datasets might have been updated since they were collected on Gapminder. So, when dealing with these updated datasets, we need to be more cautious.
| Table of Contents |
|---|
| Prerequisites 🔍📜 |
| Design 📐 |
| Conclusions 📌 |
| License 🔖 |
Prerequisites
- Python 3.6.3
- Jupyter Notebook
- Anaconda-Navigator
Design
Step One - Choose Data Set
Click this link to download the corresponding data.
Step Two - Get Organized
This project eventually contain:
- The report communicating any findings;
- Any Python code used during the analysis;
- The data set;
Step Three - Analyze
Brainstorm some questions that could be answered using the data set, then start answering those questions, we would mainly focus on looking at the relationships between multiple variables.
Conclusions
In current study, a good amount of profound analysis has been carried out. Prior to each step, deailed instructions was given and interpretions was also provided afterwards. The dataset across 41 years from 1970 to 2011. The limitations of current study was that the structure is only 275*42 in shape, thus the analysis would not be much reliable due to small scale samples. In addition, the parameters in the dataset is very simple, it only focus on the number of children out of school.
