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INSIGHTS FROM DATA
DETECT
ANOMALIES
Richard Quinn
                 Strategic Management, UCF

                 “The exam was running at a
                 grade and a half higher than it
                 had ever run before... You don’t
                 see that kind of grade
                 improvement by chance.”


      Summer
         2010
     Mid-term


          Fall
         2010
     Mid-term



“A bimodal distribution exists when an
external force is applied to the dataset
that creates a systematic bias.”
SEE THE REAL
IMPACT OF
POLICY
ENGLISH

40,000



35,000



30,000



25,000



20,000



15,000



10,000



 5,000



    0
         0   5   10   15   20   25   30   35   40   45   50   55   60   65   70   75   80   85   90   95   100
SOCIAL SCIENCE

40,000



35,000



30,000



25,000



20,000



15,000



10,000



 5,000



    0
         0   5   10   15   20   25   30   35   40   45   50   55   60   65   70   75   80   85   90   95   100
LANGUAGE

40,000



35,000



30,000



25,000



20,000



15,000



10,000



 5,000



    0
         0   5   10   15   20   25   30   35   40   45   50   55   60   65   70   75   80   85   90   95   100
SCIENCE

40,000



35,000



30,000



25,000



20,000



15,000



10,000



 5,000



    0
         0   5   10   15   20   25   30   35   40   45   50   55   60   65   70   75   80   85   90   95   100
MATHEMATICS

40,000



35,000



30,000



25,000



20,000



15,000



10,000



 5,000



    0
         0   5   10   15   20   25   30   35   40   45   50   55   60   65   70   75   80   85   90   95   100
How many subjects do students fail in? to single failures?
                   What contributes

1   5.5%                   MATHEMATICS            3.08%
2   4.3%                   COMMERCE               0.80%
3   2.3%                   ACCOUNTANCY            0.33%
4   0.9%                   PHYSICS                0.26%
5   0.3%                   ECONOMICS              0.21%
6   0.1%                   HISTORY                0.19%




    Two-subject failures
    PH Y SIC S M AT H E M AT IC S         0.79%
    PH Y SIC S C H E M IST R Y            0.77%
    C H E M IST R Y M AT H E M AT IC S    0.55%
    C O M M E R C E AC C O U N T AN C Y   0.29%
    E N GL ISH C O M M E R C E            0.17%
    B IO L O GY M AT H E M AT IC S        0.14%
WHAT
DETERMINES
PERFORMANCE?
PERFORMANCE: GIRLS                      VS   BOYS

 S u b jec t       Girs h ig h er b y    Girls   Boys
 Physic s           0                     119     119
 C he m istry       1                     123     122
 E nglish           4                     130     126
 C o m pute rs      6                     137     131
 B io lo gy         6                     129     123
 M a the m a tic s 11                     123     112
 L a ngua ge       11                     152     141
 Ac c o unting     12                     138     126
 C o m m e rc e    13                     127     114
 E c o no m ic s   16                     142     126
… and peaks for
Based on the results of the 20 lakh                                        Sep-borns
students taking the Class XII exams
                                                      The marks shoot
at Tamil Nadu over the last 3                         up for Aug borns
years, it appears that the month you
were born in can make a difference
of as much as 120 marks out of                                                   120 marks out of
                                                                                 1200 explainable
1,200.                                                                           by month of birth




                                                                     June borns
                                                                   score the lowest




                                              An identical pattern was observed in 2009 and 2010…
“It’s simply that in Canada the eligibility
cutoff for age-class hockey is January 1. A
boy who turns ten on January
2, then, could be playing alongside
someone who doesn’t turn ten until the
end of the year—and at that age, in
preadolescence, a twelve-month gap in
age represents an enormous difference in
physical maturity.”
                                              … and across districts, gender, subjects, and class X & XII.
         -- Malcolm Gladwell, Outliers
BIG DATA
REQUIRES
RICHER VISUALS
MONITORING
EFFECTIVELY
MONITORING
EFFECTIVELY
Kumaresan

  Kumar
                                                            Silambarasan

             Venkatesan
                                                                                Pandiyan
                                          Jain
  Priya
                          Ezhumalai
                                                                     Shweta
                                                 Ashwin

                                                                                      Sneha   Pooja
                                                                           Harini

                                                                     Sanjana
Manikandan



                                                           Shah                                       Varshini


                                                          Deepti




                                      Thirupathi




                                                                            Agarwal
FIND HIDDEN
CORRELATIONS
COMPARING
PERFORMANCE
EMBRACE AND LEARN FROM DATA

USE IT TO DRIVE YOUR DECISIONS

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Education: Insights from data

Editor's Notes

  1. We took the results of class 12th state board examination in Tamil Nadu and looked at the most popular names -- the top 5,000 names to be precise -- and plotted them based on their marks. The visualisation you see plotslarge boxes for the popular names. For example the big rectangle on the top left indicates people who have the name Kumar and the colour of the boxes indicate the average percentages scored by these students. The darker the blue, the higher the marks. The closer it is to white, the lower the marks. There are some fairly interesting patterns here. For example the names Jain, Shah, Agarwal and Gupta tend to score fairly high marks. These are typical north Indian names. Names like Ashwin, Shweta, Sneha, Pooja, Harini, Sanjana, Varshini, Deepti, etc they tend to score high marks as well. These are classic urban names and you’ll also notice that vast majority of them are girl’s names. Names such as Manigandan, Venkatesan, Ezhumalai, Silambarasan, Pandiyan, Kumaresan, Tirupathi, they tend to score relatively low marks. If you notice these are classic rural names and predominantly male. This is NOT an indication of marks being predicted by the names -- but rather both marks and names are a consequence of socio economic and cultural background of students.