Introduction
In modern engineering education, data-driven decision-making and problem-solving have become essential skills. As a result, the integration of Statistics, Statistical Modelling, and Data Analytics into engineering curricula has gained significant importance. These tools help engineers analyze complex systems, make informed decisions, optimize processes, and predict outcomes. The lab component of this subject provides students with practical exposure to the theories and methods used in real-world applications.
Course Objectives
| COB-1 | To impart basic knowledge about statistics, visualisation, and probability.
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| COB-2 | To impart basic knowledge about how to implement regression analysis and results.
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| COB-3 | To impart basic knowledge about how to describe classes of open and closed sets of R, concept of compactness describe metric space.
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| COB-4 | To impart basic knowledge about how to apply eigen value, eigen vectors.
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Course Outcomes
| CO | Statement | Bloom’s Level |
| DA-304P.1 | Ability to learn and understand the basic concepts about Statistics, visualisation and probability. | Understand |
| DA-304P.2 | Ability to implement regression analysis and interpret the results. Be able to fit a model to data and comment on the adequacy of the model. | Understand |
| DA-304P.3 | Ability to describe classes of open and closed sets of R, concept of compactness Describe Metric space ‐ Metric in Rn. | Evaluate, Analyze |
| DA-304P.4 | Ability to impart basic knowledge about how to apply Eigen values, Eigen vectors. | Apply |
CO-PO-PSO Mapping
| CO/PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| DA-304P.1 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 3 | 2 |
| DA-304P.2 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 3 | 2 |
| DA-304P.3 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 3 | 2 |
| DA-304P.4 | 3 | 3 | 3 | 3 | 3 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 3 | 2 |
Facilities
| Operating System /Software | ||
| Sr. No. | Name | Version |
| 1. | SCILAB and R Studio | |
| 2. | CST | NS 3.28.1 |
| Hardware | |||
| Sr.No. | Equipment Name | Specification | Quantity |
| 1. | Computer | Intel Core i9, 32 GB RAM | 25 |
| 2. | Printer | HP Laser Jet 1020 Plus | 01 |
| 3. | White Board | Standard | 01 |
Staff
- Lab Incharge: Dr. Rajiv K. Nehra
- Other Faculty Members: NA
- Lab Assistent: Mr. Shubham Kumar