Student Mental Health Report
The common saying that health is wealth is a simple statement yet loaded with many things. Human health is made up of aspects, but it can be simplified into two general parts: physical and mental health. The main focus of this report is on mental health. Mental health, as defined by the World Health Organisation (WHO), is
Mental health is a state of mental well-being that enables people to cope with the stresses of life, realise their abilities, learn and work well, and contribute to their community.
This report explores how students feel about their mental health.
DATA SOURCING AND PROFILING
The semantic model (formerly called dataset) was obtained from Kaggle. The dataset had 11 columns.
· Timestamp
· Gender
· Age
· Course of Study
· Current year of study
· Current Cumulative Grade Point Average (CGPA)
· Marital status
· Do you have depression?
· Do you suffer from anxiety?
· Do you have panic attacks?
· Did you seek any specialists for treatment?
DATA CLEANING
Microsoft Excel was used for cleaning. I checked to ensure that there were no duplicates with the Remove Duplicates from the Data Tools group, which can be found on the Data tab. The PROPER function was used to make the values for the Course of Study sentence case, as some were in uppercase, lowercase, and mixed cases. I used the “IF function” to replace values in the Martial Status; “Yes” responses were changed to married and “No” responses were changed to single. The standard formula is:
=IF(CELL ADDRESS= [REQUIRED VALUE], [NEW VALUE IF TRUE], [NEW VALUE IF FALSE])
This was what I wrote to change the values:
=IF(CELL ADDRESS=” YES”, “MARRIED”, “SINGLE”)
I also checked for spelling errors and corrected them using Spelling, which can be accessed from the Review Tab. I also observed a missing cell in the age column, and I used the average value to fill it. The Timestamp column was changed to a Short Date format.
DATA VISUALISATION
After fixing the data to top shape, I loaded it into Power BI to visualise the data. The appropriate charts were chosen to present the data in a meaningful manner.
FINDINGS
· Female students (75) were higher than males (26). Females accounted for 74.26% of the total number of students.
· Year 1 had the highest total count of age at 43, followed by Year 2, Year 3, and Year 4. Those aged 18 in Year 1 made up 15.84% of the total number of students.
· Engineering students were the highest at 26, followed by BCS and information technology. Engineering students accounted for 25.74% of students. Across all 34 courses, the number of students ranged from 1 to 26.
· The CGPA category of 3.50–4.00 had the highest number of students at 48, followed by 3.00–3.49 and 2.50–2.99. 2.00–2.49 had the lowest number of students at 2. 3.50–4.00 accounted for 47.52% of the number of students. Across all 5 CGPA categories, the number of students ranged from 2 to 48.
· The number of students who had not gone for treatment (95) was higher than that of those who went (6). Untreated students accounted for 94.06% of the total number of students.
· The number of undepressed students (66) was less than that of those who were (35). Undepressed students accounted for 65.35% of the students.
· The number of students who experienced panic attacks stood at 68, which was higher than that of those who did not (33). Panic-attacked students accounted for 67.33% of the students.
· Students who suffered from anxiety (34) were less likely than those who did not (67).
You can check the dashboard here! The filter can be used to get specific information of your interest.
Contributors: Jesupelumi Afolayan
If you have any valuable suggestions, please leave them in the comments. Thanks :)
