Course Name: Non-parametric Statistical Inference

Course abstract

In this course we shall study Non-parameteric statistical inference. This is different from parametric Statistical Inference as here the underlying distribution is assumed to be unknown. Also, these work when the population is not Normally distributed. It has major applications in many practical situations. Also, is used in Data Science and Machine Learning.


Course Instructor

Media Object

Prof.Niladri Chatterjee

Prof. Niladri Chatterjee is a Professor in Department of Mathematics, IIT Delhi. He is also the Chair Professor in Artificial Intelligence. He has more than 25 years of teaching experience in various subjects of Statistics and Computer Science. He is also the coordinator of IIT PAL Channel of Mathematics.
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Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Jul-Aug 2021

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 Syllabus

 Enrollment : 20-May-2021 to 02-Aug-2021

 Exam registration : 17-Jun-2021 to 20-Aug-2021

 Exam Date : 26-Sep-2021

Enrolled

690

Registered

17

Certificate Eligible

4

Certified Category Count

Gold

0

Silver

0

Elite

1

Successfully completed

3

Participation

6

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 3 out of 4 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
    Note:We have taken best assignment score from both July 2020 and July 2021 course
Non-parametric Statistical Inference - Toppers list

ALISHA RICHARD BANZ 73%

DWARKADAS J.SANGHVI COLLEGE OF ENGINEERING

Enrollment Statistics

Total Enrollment: 690

Registration Statistics

Total Registration : 17

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.