NPTEL: Data Science for Engineers

 with  Shankar Narasimhan and Raghunathan Rengasamy

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)


Week 1 : 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) ,
Week 2 : Linear algebra for data science (geometric view - vectors, distance, projections, eigenvalue decomposition)
Week 3 : Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance matrix)
Week 4 : Statistics (Understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates)
Week 5 : Typology of data Science problems and a solution framework
Week 6 : Univariate and multivariate linear regression Model assessment (including cross validation)
Week 7 : Verifying assumptions used in linear regression , Assessing importance of different variables, subset selection
Week 8 : Introduction to classification and classification using logistics regression ,Classification using various clustering techniques

0 Student
Cost Free Online Course
Pace Upcoming
Subject Data Science
Provider NPTEL
Language English
Calendar 8 weeks long

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