Project: Analyzing Students' Mental Health in SQL

Does going to university in a different country affect your mental health? A Japanese international university surveyed its students in 2018 and published a study the following year that was approved by several ethical and regulatory boards.

The study found that international students have a higher risk of mental health difficulties than the general population, and that social connectedness (belonging to a social group) and acculturative stress (stress associated with joining a new culture) are predictive of depression.

Explore the students data using PostgreSQL to find out if you would come to a similar conclusion for international students and see if the length of stay is a contributing factor.

Here is a data description of the columns you may find helpful.

Field Name	Description
`inter_dom`	Types of students (international or domestic)
`japanese_cate`	Japanese language proficiency
`english_cate`	English language proficiency
`academic`	Current academic level (undergraduate or graduate)
`age`	Current age of student
`stay`	Current length of stay in years
`todep`	Total score of depression (PHQ-9 test)
`tosc`	Total score of social connectedness (SCS test)
`toas`	Total score of acculturative stress (ASISS test)

DataFrameas

students

variable

-- Run this code to save the CSV file as students
SELECT * 
FROM 'students.csv';

DataFrameas

df1

variable

SELECT COUNT(*)
FROM 'students.csv'

DataFrameas

df

variable

SELECT inter_dom, COUNT(*) as total_records
FROM 'students.csv'
GROUP BY "inter_dom"

DataFrameas

df2

variable

SELECT inter_dom, COUNT(*) as total_records,AVG(todep),AVG(tosc),AVG(toas)
FROM students
WHERE inter_dom IN ('Inter', 'Dom')
GROUP BY inter_dom;

DataFrameas

df3

variable

SELECT inter_dom, COUNT(*) as total_records, AVG(todep), AVG(tosc), AVG(toas)
FROM students
GROUP BY inter_dom;

import pandas as pd

data = pd.read_csv('students.csv')
data.describe()

DataFrameas

df4

variable

SELECT inter_dom,MIN(todep) as min_phq, MAX(todep) as max_phq, ROUND(AVG(todep)) as avg_phq
FROM students
WHERE inter_dom IN ('Inter')
GROUP BY inter_dom;

DataFrameas

df5

variable

SELECT inter_dom, MIN(toas) as min_as, MAX(toas) as max_as, ROUND(AVG(toas)) as avg_as
FROM students
WHERE inter_dom IN ('Inter')
GROUP BY inter_dom;

DataFrameas

df6

variable

SELECT inter_dom,MIN(tosc) as min_sc, MAX(tosc) as max_sc, ROUND(AVG(tosc)) as avg_sc
FROM students
WHERE inter_dom IN ('Inter')
GROUP BY inter_dom;

DataFrameas

df

variable

SELECT stay,ROUND(AVG(todep),2) as average_phq, ROUND(AVG(tosc),2) as average_scs, ROUND(AVG(toas),2) as average_as
FROM students
WHERE inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay DESC