In-class Exercise/HW#1. IST-474 database.

  1. Context:

    The data were obtained in a survey of students math and portuguese language courses in secondary school. It contains a lot of interesting social, gender and study information about students. Import this data set to MS SQL Server and perform the following SQL queries on it:

    • Rename the imported table to StudentMath_TBL (10 points)
    • Select final Math grades for all students from StudentMath_TBL (10 points)
    • Select final Math grades for female students from StudentMath_TBL (10 points)
    • Select age sex, and final Math grade of the students who drink a lot of alcohol in weekdays (10 points)
    • Select age sex, and final Math grade of the students who drink a lot of alcohol in the weekend (10 points)
    • Select final Math grades of the female students who drink a lot of alcohol in the weekend (10 points)

    Content/Mata data:

    Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:

    1. school – student’s school (binary: ‘GP’ – Gabriel Pereira or ‘MS’ – Mousinho da Silveira)
    2. sex – student’s sex (binary: ‘F’ – female or ‘M’ – male)
    3. age – student’s age (numeric: from 15 to 22)
    4. address – student’s home address type (binary: ‘U’ – urban or ‘R’ – rural)
    5. famsize – family size (binary: ‘LE3’ – less or equal to 3 or ‘GT3’ – greater than 3)
    6. Pstatus – parent’s cohabitation status (binary: ‘T’ – living together or ‘A’ – apart)
    7. Medu – mother’s education (numeric: 0 – none, 1 – primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
    8. Fedu – father’s education (numeric: 0 – none, 1 – primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
    9. Mjob – mother’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
    10. Fjob – father’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
    11. reason – reason to choose this school (nominal: close to ‘home’, school ‘reputation’, ‘course’ preference or ‘other’)
    12. guardian – student’s guardian (nominal: ‘mother’, ‘father’ or ‘other’)
    13. traveltime – home to school travel time (numeric: 1 – <15 min., 2 – 15 to 30 min., 3 – 30 min. to 1 hour, or 4 – >1 hour)
    14. studytime – weekly study time (numeric: 1 – <2 hours, 2 – 2 to 5 hours, 3 – 5 to 10 hours, or 4 – >10 hours)
    15. failures – number of past class failures (numeric: n if 1<=n<3, else 4)
    16. schoolsup – extra educational support (binary: yes or no)
    17. famsup – family educational support (binary: yes or no)
    18. paid – extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
    19. activities – extra-curricular activities (binary: yes or no)
    20. nursery – attended nursery school (binary: yes or no)
    21. higher – wants to take higher education (binary: yes or no)
    22. internet – Internet access at home (binary: yes or no)
    23. romantic – with a romantic relationship (binary: yes or no)
    24. famrel – quality of family relationships (numeric: from 1 – very bad to 5 – excellent)
    25. freetime – free time after school (numeric: from 1 – very low to 5 – very high)
    26. goout – going out with friends (numeric: from 1 – very low to 5 – very high)
    27. Dalc – workday alcohol consumption (numeric: from 1 – very low to 5 – very high)
    28. Walc – weekend alcohol consumption (numeric: from 1 – very low to 5 – very high)
    29. health – current health status (numeric: from 1 – very bad to 5 – very good)
    30. absences – number of school absences (numeric: from 0 to 93)

    These grades are:

    1. G1 – first period grade (numeric: from 0 to 20)
    2. G2 – second period grade (numeric: from 0 to 20)
    3. G3 – final grade (numeric: from 0 to 20, output target)

    Source Information

    P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

    student-mat.csv

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