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In this course, you will learn :
- How to write your own useful code to perform important scientific computations. Your comprehension will be tested on a regular basis with quizzes and exercises along the way.
- Arrays, plotting, linear equations, symbolic computation, scientific algorithms, and random variables are among the topics covered in this course.
- About popular Python packages such as NumPy, Matplotlib, SciPy, and others. The application section in the course's final section will assess your ability to recall and apply the tools you've learned to newly learned scientific concepts.
Syllabus :
1. Introduction
- Introduction
- About This Course
2. Python Refresher
- Data Types and Variables
- Operators
- Conditional Statements
- Loops
- Functions
- Lambdas
- Lists
- Tuples and Dictionaries
- Using Python Packages
3. Arrays
- Vectors
- Multidimensional Arrays
- Indexing Arrays
- Array Operations
- Data Processing
- Smart Array Programming
4. Plotting
- Basic Plotting
- Important Note!
- Plotting Multiple Curves
- Setting Up the Axes
- Gallery of Graphs
- 3-D Plots
5. Systems of Linear Equations
- Building and Solving Linear Equations
- Eigenvalues and Eigenvectors
- Matrix Operations
- Sparse Matrices
6. Symbolic Computation
- Symbols and Complex Numbers
- Numerical Evaluation
- Algebraic Manipulation
- Differentiation
- Integration
- Limits
- Series Expansion
- Solving Equations
- Ordinary Differential Equations
7. Scientific Algorithms
- Numerical Integration
- Interpolation
- Polynomial Fitting
- Curve Fitting
- Optimization
- Fourier Transforms
8. Random Variables
- Random Numbers
- Flipping Coins
- Bernoulli Variable
- Normal Continuous Random Variables
- Histograms and Probability Density Function
- Percentiles
