Here we learn how to understand how mathematical models of problems arising in Engineering (and other areas) can be solved numerically.

In Numerical :

• Solve large systems of simultaneous linear equations.
• Find solutions of nonlinear equations using the bisection method, Newton’s methods, and secant method and implement them.
• Estimate the solutions of systems of first-order ordinary differential equations or higher order ordinary differential equations using various numerical methods and implement them.
• Apply these techniques to practical problems in Engineering.
• Use Prgramming language(Python) for the implementation and application of numerical methods and the visualization of results

In Statistics :

• Applying various graphical and data analysis methods for summarizing and understanding data.
• Applying various statistical models and methods for drawing conclusions and making decisions under uncertainty in engineering contexts
• Applying Prgramming language(Python) for graphical and statistical analysis.

1. Statistical analysis of data : Mean (AM, GM, HM), mode, median,standard, and quartile deviation, variance from grouped and ungrouped data, moment of a Distribution: Mean, variance, Skewness, Kurtosis
2. Solution of Equations : Solutions of algebraic and transcendental equations by Gaussian elimination / Gaussian elimination with Row pivoting, Gauss-Seidel method.
3. Find the roots of non-linear equations : Using bisection method, False position method, Newton Raphson method and Secant method.
4. Numerical differentiation and integration : Differentiation by Newton’s interpolation formula, Differentiation by forward/ backward/ central divided difference formula and Integration by Trapezoidal rule,Integration by Simpson’s 1/3 and 3/8 rule.
5. Curve fitting & Correlation : Least square regression (line fitting), Least square parabola, Fitting transcendental equation and Linear correlation and Rank correlation.