Data Science for Engineers

Exam Preparation PDF

ABOUT THE COURSE :

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.

Summary

Course Status :	Ongoing
Course Type :	Elective
Duration :	8 weeks
Category :	Computer Science and Engineering Data Science Programming
Credit Points :	2
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	15 Sep 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	21 Aug 2023
Exam Date :	24 Sep 2023 IST

Course layout

Week 1:  Course philosophy and introduction to R  

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

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

Books and references

1. INTRODUCTION TO LINEAR ALGEBRA - BY GILBERT STRANG
   
2. APPLIED STATISTICS AND PROBABILITY FOR ENGINEERS – BY DOUGLAS MONTGOMERY

Exam Preparation PDF

Part : 1

   

Part : 2

   

Part : 3