NumPy Syllabus

Welcome to The Coding College, your one-stop resource for coding tutorials and guides. This NumPy Syllabus provides a structured learning path to master the essential and advanced concepts of NumPy, a fundamental Python library for numerical computing.

This syllabus is designed for beginners, intermediates, and advanced learners aiming to enhance their data manipulation and computational skills using NumPy.

NumPy Syllabus Outline

1. Introduction to NumPy

• What is NumPy?
• Importance of NumPy in Data Science and Machine Learning.
• Installing and Importing NumPy.
• NumPy vs Python Lists.

2. NumPy Basics

• Creating Arrays.
  • One-dimensional Arrays.
  • Two-dimensional Arrays.
  • Multi-dimensional Arrays.
• Understanding Array Attributes:
  • shape, size, ndim, dtype.
• Data Types in NumPy Arrays.

3. Array Operations

• Basic Arithmetic Operations on Arrays.
• Broadcasting in NumPy.
• Universal Functions (ufuncs):
  • Mathematical Operations: Add, Subtract, Multiply, Divide.
  • Aggregations: Sum, Min, Max, Mean, Standard Deviation.

4. Indexing and Slicing

• Accessing Array Elements using Indexing.
• Slicing Arrays to Extract Subarrays.
• Boolean Indexing and Conditional Selection.

5. Advanced Array Manipulations

• Reshaping Arrays.
• Joining Arrays (Concatenation).
• Splitting Arrays.
• Copy vs View: Understanding Array References.

6. Random Numbers with NumPy

• Generating Random Numbers.
• Random Data Distributions:
  • Uniform Distribution.
  • Normal (Gaussian) Distribution.
  • Binomial, Poisson, and Other Distributions.
• Random Permutations and Shuffling.

7. Linear Algebra with NumPy

• Dot Product and Matrix Multiplication.
• Determinants and Inverses of Matrices.
• Eigenvalues and Eigenvectors.
• Solving Systems of Linear Equations.

8. Working with Polynomials

• Representing Polynomials with NumPy.
• Evaluating Polynomials.
• Finding Roots and Derivatives of Polynomials.

9. Trigonometric and Statistical Functions

• Trigonometric Operations:
  • Sine, Cosine, Tangent.
  • Hyperbolic Functions.
• Statistical Functions:
  • Mean, Median, Variance, Standard Deviation.
• Histograms and Data Analysis.

10. File Input/Output with NumPy

• Saving Arrays to Files.
  • save and load Functions.
  • Text Files with savetxt and loadtxt.
• Reading Data into NumPy Arrays.

11. NumPy Broadcasting

• What is Broadcasting?
• Applying Broadcasting to Arrays of Different Shapes.
• Common Use Cases of Broadcasting.

12. Performance Optimization with NumPy

• Vectorization: Replacing Loops with NumPy Operations.
• Avoiding Common Pitfalls for Better Performance.
• Comparing NumPy with Pandas and Other Libraries.

13. NumPy Universal Functions (ufuncs)

• Introduction to ufuncs.
• Built-in NumPy Functions.
• Creating Custom ufuncs.

14. NumPy Integration with Other Libraries

• Pandas and NumPy.
• Matplotlib for Visualization.
• SciPy for Advanced Numerical Computing.
• TensorFlow and PyTorch for Machine Learning.

15. NumPy Quiz and Exercises

• Hands-on Exercises for Beginners.
• Intermediate and Advanced-Level Problems.
• NumPy Certification Practice Quiz.

16. Real-World Applications of NumPy

• Data Cleaning and Preparation.
• Signal Processing.
• Image Manipulation and Analysis.
• Financial and Statistical Modeling.

How to Use this Syllabus

1. Beginner Path: Focus on Sections 1-5 to build a strong foundation.
2. Intermediate Path: Dive into Sections 6-10 to enhance your understanding.
3. Advanced Path: Explore Sections 11-16 to master NumPy for real-world applications.

Additional Learning Resources

For more in-depth guides and tutorials, visit The Coding College. Our website offers structured lessons, quizzes, and exercises to help you solidify your knowledge.