This is a project to analyze which factors influence the performance of IT students of the Digital Metropolis Institute (IMD) and Department of Informatics and Applied Mathematics (DIMAp) of the Federal University of Rio Grande do Norte. The code were implemented in Python 3 and in Jupyter Notebook. We will be using knowledges of:
- Exploratory data analysis
- Exploratory data visualization
- Statistical data visualization
- Data visualization with Seaborn
- Numpy
- Pandas
- Matplotlib
- Seaborn
Our objective was based on identifying what influences students' yield by analysing the number of students by class, teachers admission date, absences, class shift and departament from 2014.1 until 2017.2.
The project was splitted in some sessions:
- Importing Libraries
- Reading the open data from http://dados.ufrn.br
- Data preparation
- Merge and concatenation of the dataframes
- Study
- Conclusion
Developed by Aroldo Felix (junioraroldo37@gmail.com), George Franklin (georgefranklinbti@gmail.com) and Julia Ferreira (juliafsouzag@gmail.com).
- http://dados.ufrn.br/
- http://seaborn.pydata.org/tutorial.html
- http://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facet%20grid#seaborn.FacetGrid
- https://matplotlib.org/api/pyplot_summary.html
- https://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html
- https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.math.html