IIT Kharagpur - Data Science for Engineers by NPTEL
4.8 /5- Offered byNPTEL
- Public/Government Institute
Data Science for Engineers by NPTEL at NPTEL Overview
Duration | 8 weeks |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Science for Engineers by NPTEL at NPTEL Highlights
- Offered by IIT Madras
- Final score comprises of 25% of average of best 6 assignments and 75% of the proctored certification exam score out of 100
- Enrollments start from 20 July 2020
- Course conducted byProf. Ragunathan Rengasamy & Prof. Shankar Narasimhan (IIT Madras alumnus)
- Enroll for free
- Pay for Certification Examination
Data Science for Engineers by NPTEL at NPTEL Course details
- 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)
- This course introduction of - R as a programming language , mathematical foundations required for data science, first level data science algorithms, data analytics problem solving framework and a practical capstone case study
Data Science for Engineers by NPTEL at NPTEL Curriculum
Week 1:
Course philosophy and introduction to R
Week 2:
Linear algebra for data science
Week 3:
Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariancematrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidenceinterval 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
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