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![MATLAB Example
• Example: Solve a system of linear equations
• A = [1 2; 3 4]
• B = [5; 6]
• X = AB
• This solves the system of equations and
returns the values of X.](https://image.slidesharecdn.com/cs301detailedoverviewmatlabmathematicapython-241002195523-fd3b7388/85/CS301_Detailed_Overview_MATLAB_Mathematica_Python-pptx-4-320.jpg)
![Mathematica Example
• Example: Symbolic differentiation
• D[Sin[x]^2, x]
• This will compute the derivative of Sin[x]^2
symbolically and return the result.](https://image.slidesharecdn.com/cs301detailedoverviewmatlabmathematicapython-241002195523-fd3b7388/85/CS301_Detailed_Overview_MATLAB_Mathematica_Python-pptx-7-320.jpg)
![2Essential-Python-Libraries-for-Data-Analytics[1].pdf](https://cdn.slidesharecdn.com/ss_thumbnails/2essential-python-libraries-for-data-analytics1-250722104424-d73307c5-thumbnail.jpg?width=640&height=640&fit=bounds)
CS301_Detailed_Overview_MATLAB_Mathematica_Python.pptx
- 1. Detailed Overview ofMATLAB, Mathematica, and Python For 5th Semester Students - CS-301
- 2. Course Objective • Thiscourse will help students solve physical and engineering problems by using MATLAB, Mathematica, and Python for computational and data-driven problem-solving.
- 3. Introduction to MATLAB •MATLAB is a high-level language for numerical computation, visualization, and programming. It is especially powerful for matrix-based computations.
- 4. MATLAB Example • Example:Solve a system of linear equations • A = [1 2; 3 4] • B = [5; 6] • X = AB • This solves the system of equations and returns the values of X.
- 5. MATLAB Applications • MATLABis used in control system design, digital signal processing, and image processing, making it indispensable for engineers.
- 6. Introduction to Mathematica •Mathematica is known for symbolic computation and is widely used in academic and industrial research.
- 7. Mathematica Example • Example:Symbolic differentiation • D[Sin[x]^2, x] • This will compute the derivative of Sin[x]^2 symbolically and return the result.
- 8. Mathematica Applications • Mathematicais ideal for performing symbolic calculations, solving differential equations, and generating advanced visualizations.
- 9. Introduction to Python •Python is a versatile language used in data analysis, web development, and scientific computing, known for its simplicity and robust libraries.
- 10. Python Example • Example:Data analysis with Pandas • import pandas as pd • data = pd.read_csv('file.csv') • summary = data.describe() • This reads a CSV file and generates a summary of the data.
- 11. Python Applications • Pythonis used in data science, machine learning, automation, and backend development. Libraries like NumPy and Pandas make data handling easy.
- 12. Comparison of Tools •MATLAB: Best for matrix manipulations and numerical solutions. • Mathematica: Best for symbolic calculations and research. • Python: Versatile, with strong libraries for data and scientific computing.
- 13. Real-World Use Cases •1. MATLAB: Used in control systems and robotics. • 2. Mathematica: Applied in physics research for simulations. • 3. Python: Data science and machine learning, powering AI models.
- 14. Summary • In thiscourse, students will gain hands-on experience in problem-solving with these tools, focusing on how to approach engineering and mathematical problems.
- 15.