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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 NameDescription
inter_domTypes of students (international or domestic)
japanese_cateJapanese language proficiency
english_cateEnglish language proficiency
academicCurrent academic level (undergraduate or graduate)
ageCurrent age of student
stayCurrent length of stay in years
todepTotal score of depression (PHQ-9 test)
toscTotal score of social connectedness (SCS test)
toasTotal score of acculturative stress (ASISS test)
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DataFrameas
students
variable
-- Run this code to save the CSV file as students
SELECT * 
FROM 'students.csv';

Task 1: Count all records, count all records per student type

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DataFrameas
df
variable
-- Count of all records
SELECT COUNT(*) AS count_of_all_records
FROM 'students.csv';
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DataFrameas
df1
variable
-- Count all records per student type
SELECT inter_dom,COUNT(inter_dom) AS count_per_student
FROM 'students.csv'
GROUP BY inter_dom;

Task 2: Filter data to see how it differs between student types

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DataFrameas
df2
variable
-- Queries will be grouped by academic level, gender, region, inter/dom status

-- Academic level
SELECT academic, 
	ROUND(AVG(age),2) AS avg_age_per_level, 
	ROUND(AVG(japanese),2) AS avg_jap_score_per_level, 
	ROUND(AVG(english),2) AS avg_eng_score_per_level
FROM 'students.csv'
GROUP BY academic
ORDER BY academic;
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DataFrameas
df3
variable
-- Gender
SELECT gender, 
	ROUND(AVG(age),2) AS avg_age_per_level, 
	ROUND(AVG(japanese),2) AS avg_jap_score_per_level, 
	ROUND(AVG(english),2) AS avg_eng_score_per_level
FROM 'students.csv'
GROUP BY gender
ORDER BY gender;
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DataFrameas
df4
variable
-- Region
SELECT region, 
	ROUND(AVG(age),2) AS avg_age_per_level, 
	ROUND(AVG(japanese),2) AS avg_jap_score_per_level, 
	ROUND(AVG(english),2) AS avg_eng_score_per_level
FROM 'students.csv'
GROUP BY region
ORDER BY region;
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DataFrameas
df5
variable
-- Inter/Dom
SELECT inter_dom, 
	ROUND(AVG(age),2) AS avg_age_per_level, 
	ROUND(AVG(japanese),2) AS avg_jap_score_per_level, 
	ROUND(AVG(english),2) AS avg_eng_score_per_level
FROM 'students.csv'
GROUP BY inter_dom
ORDER BY inter_dom;

Task 3: Summary Statistics of diagnostic tests

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DataFrameas
df6
variable
-- Depression Score
SELECT ROUND(MIN(todep),2) AS min_dep_score, 
	ROUND(MAX(todep),2) AS max_dep_score, 
	ROUND(AVG(todep),2) AS avg_dep_score
FROM 'students.csv';
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DataFrameas
df7
variable
-- Social Connectedness
SELECT ROUND(MIN(tosc),2) AS min_sc_score, 
	ROUND(MAX(tosc),2) AS max_sc_score, 
	ROUND(AVG(tosc),2) AS avg_dsc_score
FROM 'students.csv';