Data Science for Engineers

Week by week (official course layout)

1. Week 1: Course philosophy and introduction to R
2. Week 2: Linear algebra for data science 1. Algebraic view - vectors, matrices, product of matrix & vector, rank, null space, solution of over-determined set of equations and pseudo-inverse) 2. Geometric view - vectors, distance, projections, eigenvalue decomposition
3. 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)
4. Week 4: Optimization
5. Week 5: 1. Optimization 2. Typology of data science problems and a solution framework
6. 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
7. Week 7: Classification using logistic regression
8. Week 8: Classification using kNN and k-means clustering

Topics as the course page lists them. Weekly assignment due dates are shown only inside the course after a SWAYAM sign-in, so they are not here — check the course page every week.

This course's certificate rule

Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100 Final score = Average assignment score + Exam score Please note that assignments encompass all types (including quizzes, programming tasks, and essay submissions) available in the specific week. YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

As printed on the course page. General rules: NPTEL FAQ.