Data Science for Engineers


Prof. Raghunathan Rengaswamy

Prof. Shankar Narasimhan

IIT Madras

*Additional GST and optional Exam fee are applicable.

SKU: IIT Madras Category:



  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


  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)


Any interested learner


10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.


Prior to joining IIT Madras as a professor, Prof.Rengaswamy was a professor of Chemical Engineering and Co-Director of the Process Control and Optimization Consortium at Texas Tech University, Lubbock, USA. He was also a professor and associate professor at Clarkson University, USA and an assistant professor at IIT Bombay. His major research interests are in the areas of fault detection and diagnosis and development of data science algorithms for manufacturing industries.

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.


Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 2000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

Assignment score = Score more than 50% in at least 6/8 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.


Course Details

Week 1: Course philosophy and introduction to R
Week 2: Linear algebra for data science
Algebraic view – vectors, matrices, product of matrix & vector, rank, null space, solution of over-determined set of equations and pseudo-inverse)
Geometric view – vectors, distance, projections, eigenvalue decomposition
Week 3: Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance matrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates)
Week 4: Optimization
Week 5:

1. Optimization
2. Typology of data science problems and a solution framework
Week 6:

1. Simple linear regression and verifying assumptions used in linear regression
2. Multivariate linear regression, model assessment, assessing importance of different variables, subset selection
Week 7: Classification using logistic regression
Week 8: Classification using kNN and k-means clustering


  • Introduction To Linear Algebra – By Gilbert Strang
  • Applied Statistics And Probability For Engineers – By Douglas Montgomery


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