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Data Science for Engineers

Overview

Learning Objectives :Introduce R as a programming languageIntroduce the mathematical foundations required for data scienceIntroduce the first level data science algorithmsIntroduce a data analytics problem solving frameworkIntroduce a practical capstone case studyLearning Outcomes:Describe a flow process for data science problems (Remembering)Classify data science problems into standard typology (Comprehension)Develop R codes for data science solutions (Application)Correlate results to the solution approach followed (Analysis)Assess the solution approach (Evaluation)Construct use cases to validate approach and identify modifications required (Creating)INTENDED AUDIENCE: Any interested learnerPREREQUISITES: 10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.INDUSTRY SUPPORT: HONEYWELL, ABB, FORD, GYAN DATA PVT. LTD.

Syllabus

Week 1: Course philosophy and introduction to RWeek 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
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: OptimizationWeek 5: 1. Optimization 2. Typology of data science problems and a solution frameworkWeek 6: 1. Simple linear regression and verifying assumptions used in linear regression 2. Multivariate linear regression, model assessment, assessing importance of different variables, subset selectionWeek 7: Classification using logistic regressionWeek 8: Classification using kNN and k-means clustering

Taught by

Shankar Narasimhan and Raghunathan Rengasamy

Reviews

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• Anonymous

Anonymous is taking this course right now.

The course is not good. This course is full on theory lectures and the teacher is too boring with little or no information about the practical use of the math we are studying.

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