Project Description
The project is developed from scratch as a team with anonymised live student data from the Academic year 2018-2019. This project aims to design and develop web-based interactive data visualisation using JavaScript, HTML, CSS and d3js. This is a lightweight web-based application that enables the end-user to read the information from hugely available data more easily with the help of interactive graphics.
Data Pre-Processing
Technical Implementation
- JSON file – The raw data was provided in a JSON file, which had lots of missing, duplicate and corrupt data as it was captured from the live environment.
- Power BI – We have used Power BI to understand and do the basic data processing. This data cleaning process was centralised with all members of the team.
- Python – we have implemented python code to convert the CSV files to JSON and vice versa, and also to analyse the data (as live data was huge)
Exploratory Data Analysis
- Understand and visualise the different aspects of the real-time data
- Identify the research questions and how the data can be presented
- Identify the appropriate visuals/graphs
Analysis
1. Visual 1: A COMPARISON BETWEEN NUMBER OF FEMALE AND MALE STUDENTS
Purpose: This visual is addressing the difference between the number of Male and Female Students in the university, then expand to different departments and age groups.
Functionality: This is an interactive sequence sunburst chart presenting the percentage of students by gender in each department and age group.
2. Visual 2: PERFORMANCE COMPARISON BY GENDER AND AGE GROUPS
Purpose: This visual is addressing the difference in students’ performance in term of average marks and grade by gender and age groups.
Functionality: There are two graphs in this visual. The first graph is an interactive lollipop illustrating the average marks by gender and age groups. The second graph is an interactive bar chart showing the number of students for each type of grade. There is an option to easily switching between the Stacked and Grouped bar chart to help users investigate the information.
ABOUT ME
Course
Applied Data Science
Biography
I am a highly motivated Data Analyst with strong experience in data analysis, financial and management reporting, and reporting process automation. I’m currently studying for a Master of Applied Data Science, and my interest is to find meaningful patterns in the data and design report dashboard. I have strong analytical skills, attention to detail, and work well in a team as well as independently.
Software & Hardware Proficiencies
Python, R, SQL, JavaScript, Power BI, TensorFlow, Keras, Scikit-learn, Jupyter, Tkinter, D3.js, Microsoft Office, Visual Studio, SPSS, SmartPLS, IBM Cognos Analytics.
Employment, Work Experience
I currently a student ambassador support delivering business intelligence with the Power BI course. I also involved in designing the Power BI student workbook. Previously, I was a reporting accountant at AB InBev South East Asia and an associate auditor at EY Vietnam.
