Course Name: Data Science for Engineers

Course abstract

Learning Objectives : 1. Introduce R as a programming language 2. Introduce the mathematical foundations required for data science 3. Introduce the first level data science algorithms 4. Introduce a data analytics problem solving framework 5. Introduce a practical capstone case study Learning Outcomes: 1. Describe a flow process for data science problems (Remembering) 2. Classify data science problems into standard typology (Comprehension) 3. Develop R codes for data science solutions (Application) 4. Correlate results to the solution approach followed (Analysis) 5. Assess the solution approach (Evaluation) 6. Construct use cases to validate approach and identify modifications required (Creating)


Course Instructor

Media Object

Prof. Shankar Narasimhan

Prof.Shankar Narasimhan is currently a professor in the department of Chemical Engineering at IIT Madras. His major research interests are in the areas of data mining, process design and optimization, fault detection and diagnosis and fault tolerant control. He has co-authored several important papers and a book titled Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data which has received critical appreciation in India and abroad.


Teaching Assistant(s)

K ISWARYA

M.Sc

 Course Duration : Jan-Mar 2020

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 Syllabus

 Enrollment : 18-Nov-2019 to 03-Feb-2020

 Exam registration : 16-Dec-2019 to 21-Feb-2020

 Exam Date : 29-Mar-2020

Enrolled

15192

Registered

Certificate Eligible

Will be announced

Certified Category Count

Gold

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Elite

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Successfully completed

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Participation

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Success

Elite

Gold





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Final Score Calculation Logic

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Enrollment Statistics

Total Enrollment: 15192

Registration Statistics

Total Registration : 1691

Assignment Statistics




Assignment Score

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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.