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Complete NumPy course - Data Science in Python
Rating: 4.2 out of 5(67 ratings)
1,377 students

Complete NumPy course - Data Science in Python

Master Python's central data science and scientific computing library: NumPy
Created byJavier Ventajas
Last updated 5/2025
English
English [Auto],

What you'll learn

  • How to solve math / statistics problems using NumPy.
  • Perform the most common array manipulation operations in Machine Learning / Data Science.
  • Solve problems common to linear algebra, statistics and image processing using the NumPy library.

Course content

3 sections • 16 lectures • 1h 43m total length

  • Before we begin2:13

    Learn to manipulate large data arrays with NumPy, a foundational skill for data science and machine learning, as you prepare for linear models and data visualization.

  • Our complete course catalog0:13
  • Connect with me on social media0:06
  • Introduction to NumPy12:02

    Discover how NumPy uses n-dimensional arrays to represent vectors, matrices, and tensors, enabling memory-efficient, vectorized operations on large data for data science, image processing, and more.

  • Creating, indexing and slicing NumPy arrays9:54

    Create and inspect NumPy arrays using the array function, build a two row matrix, and explore indexing, slicing, boolean masks, and the nonzero function to locate even elements.

  • Copying and editing NumPy arrays4:08

    Master creating a numpy zero-filled array with zeros_like, learn why references share data, and use copy to edit elements independently without affecting the original array.

  • Stacking and restructuring NumPy arrays13:40

    Explore how to change arrays' shape using stacking, splitting, and reshaping in numpy, including vstack, hstack, stack, vsplit, hsplit, expand_dims, np.newaxis, reshape, and flatten.

  • Arithmetic operations with NumPy arrays12:18

    Learn to perform numpy array arithmetic using full and full_like, including add, subtract, multiply, divide, and exponentiation; explore axis-based sums and a normal distribution plot.

  • Operations with NumPy arrays of different shapes4:39

    Learn how NumPy broadcasting rules apply a single element across arrays with different shapes during operations, illustrated by range-based multiplications, vector additions, and a 4x4 matrix with reshaped vectors.

  • Concatenation, reversion and persistence of NumPy arrays8:49

    Master numpy operations: find min and max, sort with axis, use argsort, create ones, identify unique values, concatenate and flip arrays, and save or load in npy or csv.

Requirements

  • Basic notions of Python programming.

Description

In this course you will learn to use the NumPy library fluently. NumPy is a numerical computation library extensively used in data science, machine learning and statistics. In fact, many other libraries in these fields rely on NumPy arrays to deliver their functionality efficiently. In the area of data science and machine learning we often work with tabular data, which can be represented very well by NumPy arrays. In the course you will learn how to work with n-dimensional arrays and how to manipulate them comfortably to solve complex tasks in different domains.


NumPy processes matrix operations extremely efficiently, offering low execution time and memory usage. Its functionality is implemented in the C programming language: a very efficient compiled language. This functionality is executed from the Python interface with a simple declarative syntax.


The course is divided into 12 lessons:

- Introduction to the NumPy library.

- Creating, indexing and slicing NumPy arrays.

- Copying and editing NumPy arrays.

- Stacking and restructuring NumPy arrays.

- Arithmetic operations with NumPy arrays.

- Operations with NumPy arrays of different shapes.

- Concatenation, reversion and persistence of NumPy arrays.

- Applications of NumPy - Random number generation

- Applications of NumPy - Statistics

- Applications of NumPy - Linear algebra

- Applications of NumPy - Image manipulation

- Applications of NumPy  - Chaotic dynamical systems


At the end of the course, you will know how to create arrays using different methods, manipulate them and perform mathematical operations with them.

Who this course is for:

  • Data science students.
  • Professionals of any scientific or engineering discipline.
  • Programmers interested in machine learning.
  • Data analysts interested in expanding their knowledge.

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