# Data Science for Engineers

Por: Swayam . en: ,

## Overview

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)

INTENDED AUDIENCE: Any interested learner
PREREQUISITES: 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

### COURSE LAYOUT

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 decompositionWeek 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

### Plataforma 