Complete Numpy Course

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GreatToCode: Complete NumPy Course (Basic to Advanced)

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Course Title: Mastering NumPy – The Complete Guide to Scientific Computing with Python

Course Description: This course offers an in-depth understanding of NumPy, the foundational library for numerical computing in Python. Learn how to manipulate arrays, perform mathematical operations, and build high-performance data processing solutions used in data science, AI, ML, and scientific research.

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Module 1: Introduction to NumPy

1. What is NumPy?
2. Why Use NumPy?
3. Installing NumPy
4. Importing and Checking the Version
5. NumPy vs Lists in Python

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Module 2: Working with Arrays

1. Creating 1D, 2D, 3D Arrays
2. Array Indexing and Slicing
3. Array Data Types and Type Conversion
4. Array Reshaping and Transposing
5. Copy vs View in Arrays

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Module 3: Array Operations

1. Arithmetic Operations
2. Broadcasting and Vectorization
3. Aggregation Functions (sum, mean, std, etc.)
4. Logical Operations and Boolean Indexing
5. Sorting, Searching, and Counting

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Module 4: Advanced Array Manipulation

1. Stacking and Splitting Arrays
2. Tiling and Repeating
3. Axis and Dimensionality Management
4. Unique Values and Set Operations
5. Masking and Conditional Extraction

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Module 5: Mathematical and Statistical Functions

1. Basic Math Operations
2. Trigonometric and Exponential Functions
3. Rounding and Clipping
4. Linear Algebra with NumPy
5. Matrix Multiplication and Dot Products

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Module 6: Random Module in NumPy

1. Random Number Generation
2. Seeding and Reproducibility
3. Distributions (normal, binomial, etc.)
4. Random Sampling and Shuffling
5. Creating Simulations

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Module 7: Working with Missing or Invalid Data

1. NaNs and Infs in NumPy
2. Detecting Missing Data
3. Handling Missing Data
4. Replacing and Filtering
5. Using NumPy with Pandas

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Module 8: Performance and Memory Management

1. Performance Comparison with Python Lists
2. In-Place Operations
3. Memory Layout and Efficiency
4. Vectorization Tips
5. Using Numexpr and Cython with NumPy

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Module 9: Real-World Projects with NumPy

1. Financial Data Analysis
2. Image Processing using Arrays
3. Simulation of Dice Games
4. Scientific Computing with NumPy
5. Capstone Project – Data Pipeline with NumPy

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Module 10: Certification and Resources

1. Final Quiz and Assignments
2. Downloadable Cheat Sheets
3. GitHub Repository
4. Certificate of Completion
5. Access to NumPy Coding Challenges and Community

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Extras:

• NumPy Interview Questions
• Integration with Pandas, Matplotlib, and SciPy
• Weekly Practice Projects
• Resume Tips for Data Science/Analytics Roles

Master the backbone of numerical computing in Python – Learn NumPy with GreatToCode!