Project: Analyzing Students' Mental Health in SQL
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    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 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)
    Unknown integration
    DataFrameavailable as
    students
    variable
    -- Run this code to save the CSV file as students
    SELECT * 
    FROM 'students.csv';
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Start by counting all of the records in the data

    Unknown integration
    DataFrameavailable as
    df
    variable
    SELECT COUNT(*)
    FROM 'students.csv';
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Then count all records per student type to see how the records are categorized and scored

    Unknown integration
    DataFrameavailable as
    df1
    variable
    SELECT inter_dom, COUNT(inter_dom) AS student_type
    FROM 'students.csv'
    GROUP BY inter_dom;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Filter the data to see how it differs between the student types

    Unknown integration
    DataFrameavailable as
    df2
    variable
    SELECT academic, age, stay, japanese, todep, tosc, apd, ahome
    FROM 'students.csv'
    WHERE inter_dom = 'Inter';
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Find the summary statistics of the diagnostic tests for all students using aggregate functions, rounding the test scores to two decimal places, remembering to use aliases

    Unknown integration
    DataFrameavailable as
    df3
    variable
    SELECT inter_dom,
    	ROUND(AVG(todep),2) AS average_depression, 
    	ROUND(AVG(tosc),2) AS average_social_connectedness,
    	ROUND(AVG(toas),2) AS average_acculterative_stress
    FROM 'students.csv'
    GROUP BY inter_dom;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    Repeat this to summarize the data for international students only

    Unknown integration
    DataFrameavailable as
    df4
    variable
    SELECT 
    	ROUND(AVG(todep),2) AS average_depression, 
    	ROUND(AVG(tosc),2) AS average_social_connectedness,
    	ROUND(AVG(toas),2) AS average_acculterative_stress
    FROM 'students.csv'
    WHERE inter_dom = 'Inter';
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.