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Numpy

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NumPy: High-Speed Numerical Computing for Data Science

Unlock the full potential of data processing in Python. This course dives deep into NumPy, the foundational library that powers almost every data science tool, enabling you to perform complex mathematical operations at lightning speed.

Who is this course for?

This course is for learners who have a basic understanding of Python. It's an essential next step for aspiring data scientists and analysts who want to understand the engine behind libraries like Pandas and perform efficient numerical computations.

What You Will Learn:

  • Master the NumPy ndarray, the core of numerical computing in Python.

  • Create and manipulate multi-dimensional arrays with ease.

  • Perform lightning-fast vectorized operations, eliminating slow Python loops.

  • Apply powerful statistical and linear algebra functions to entire datasets.

  • Utilize broadcasting to perform calculations between arrays of different shapes.

  • Select, slice, and filter data within NumPy arrays using advanced indexing techniques.

Detailed Course Curriculum

Module 1: The NumPy Array

  • 1.1 Why NumPy? Speed and Efficiency.

  • 1.2 Creating NumPy Arrays: From lists, arange(), linspace(), zeros(), ones().

  • 1.3 Array Attributes: shape, dtype, ndim, size.

  • 1.4 The importance of a single data type in an array.

Module 2: Array Indexing and Manipulation

  • 2.1 1D, 2D, and 3D Array Indexing and Slicing.

  • 2.2 Boolean Array Indexing: Filtering data based on conditions.

  • 2.3 Fancy Indexing: Accessing multiple array elements at once.

  • 2.4 Reshaping Arrays: reshape(), flatten(), ravel().

Module 3: Universal Functions (ufuncs) and Broadcasting

  • 3.1 The Power of Vectorization: Ditching slow for loops.

  • 3.2 Mathematical Operations: np.sqrt, np.exp, np.log.

  • 3.3 Statistical Operations: np.mean, np.median, np.std, np.sum.

  • 3.4 Broadcasting: The magic of operations on arrays of different sizes.

Module 4: Real-World Applications

  • 4.1 Basic Linear Algebra: Matrix multiplication, dot products.

  • 4.2 Working with random numbers for simulations.

  • 4.3 Practical Example: Analyzing stock price changes.

Hands-on Project:

  • Hands-on Project on real data sets

Prerequisites:

  • Basic understanding of Python, including data structures (lists) and functions.

Certification

Earn a Certificate of Completion powered by DigData — trusted by professionals, valued by employers, and aligned with real-world industry needs.

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