• For Individuals
  • For Businesses
  • For Universities
  • For Governments
Coursera
  • Online Degrees
  • Careers
  • Log In
  • Join for Free
    Coursera
    • Browse
    • Numpy

    NumPy Courses Online

    Learn NumPy for numerical computing in Python. Understand array operations, mathematical functions, and data manipulation using NumPy.

    Skip to search results

    Filter by

    Subject
    Required
     *

    Language
    Required
     *

    The language used throughout the course, in both instruction and assessments.

    Learning Product
    Required
     *

    Build job-relevant skills in under 2 hours with hands-on tutorials.
    Learn from top instructors with graded assignments, videos, and discussion forums.
    Get in-depth knowledge of a subject by completing a series of courses and projects.
    Earn career credentials from industry leaders that demonstrate your expertise.
    Complete graduate-level learning without committing to a full degree program.

    Level
    Required
     *

    Duration
    Required
     *

    Skills
    Required
     *

    Subtitles
    Required
     *

    Educator
    Required
     *

    Explore the NumPy Course Catalog

    • U

      University of Michigan

      Applied Plotting, Charting & Data Representation in Python

      Skills you'll gain: Matplotlib, Plot (Graphics), Data Visualization Software, Interactive Data Visualization, Scientific Visualization, Visualization (Computer Graphics), Statistical Visualization, Graphing, Scatter Plots, Data Manipulation, Histogram, NumPy, Pandas (Python Package)

      4.5
      Rating, 4.5 out of 5 stars
      ·
      6.3K reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      Deep Learning and Reinforcement Learning

      Skills you'll gain: Generative AI, Reinforcement Learning, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Unsupervised Learning, Artificial Neural Networks, PyTorch (Machine Learning Library), Keras (Neural Network Library), Machine Learning Algorithms, Tensorflow, Computer Vision, Dimensionality Reduction, Network Architecture, Natural Language Processing, NumPy

      4.6
      Rating, 4.6 out of 5 stars
      ·
      251 reviews

      Intermediate · Course · 1 - 3 Months

    • I

      IBM

      Unsupervised Machine Learning

      Skills you'll gain: Unsupervised Learning, Dimensionality Reduction, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Feature Engineering, Machine Learning, Statistical Machine Learning, Text Mining, Data Mining, Data Science, Unstructured Data, Big Data, NumPy, Data Analysis, Natural Language Processing, Linear Algebra

      4.7
      Rating, 4.7 out of 5 stars
      ·
      313 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      Universidad Nacional Autónoma de México

      Python: de cero a analista de datos

      Skills you'll gain: Exploratory Data Analysis, Matplotlib, Pandas (Python Package), Extract, Transform, Load, Data Analysis, NumPy, Package and Software Management, Time Series Analysis and Forecasting, Data Visualization Software, Data Science, Python Programming, Jupyter, Graphing, Data Processing, Data Import/Export, Data Manipulation, Scripting, Software Installation, Computational Thinking, Mac OS

      4.1
      Rating, 4.1 out of 5 stars
      ·
      36 reviews

      Beginner · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      Logistic Regression with NumPy and Python

      Skills you'll gain: Matplotlib, Data Visualization, Seaborn, Exploratory Data Analysis, NumPy, Data Analysis, Jupyter, Machine Learning, Python Programming, Supervised Learning, Regression Analysis, Algorithms

      4.5
      Rating, 4.5 out of 5 stars
      ·
      393 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • D

      DeepLearning.AI

      Linear Algebra for Machine Learning and Data Science

      Skills you'll gain: Linear Algebra, NumPy, Dimensionality Reduction, Jupyter, Data Manipulation, Data Science, Machine Learning Algorithms, Applied Mathematics, Python Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • R

      Rice University

      Python Data Visualization

      Skills you'll gain: Data Visualization Software, Plot (Graphics), Package and Software Management, Python Programming, Data Cleansing, Software Installation, Scripting, Data Manipulation, Data Processing, Data Integration, Data Import/Export, Program Development, Technical Documentation, Scripting Languages, Data Structures, Software Documentation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      542 reviews

      Beginner · Course · 1 - 4 Weeks

    • C

      Codio

      Python Object Basics: Functions, Recursion, and Objects

      Skills you'll gain: Object Oriented Programming (OOP), Programming Principles, Computer Programming, Scripting Languages, Python Programming, Scripting

      4.5
      Rating, 4.5 out of 5 stars
      ·
      65 reviews

      Intermediate · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Python for Genomic Data Science

      Skills you'll gain: Bioinformatics, Data Structures, Jupyter, Python Programming, Programming Principles, Scripting Languages, Scripting, Package and Software Management, Computer Programming, Data Manipulation, File Management

      4.3
      Rating, 4.3 out of 5 stars
      ·
      1.8K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: New
      New
      P

      Packt

      NumPy, Matplotlib & Pandas – Data Science Prerequisites

      Skills you'll gain: Matplotlib, NumPy, Pandas (Python Package), Data Visualization Software, Data Manipulation, Python Programming, Scatter Plots, Scikit Learn (Machine Learning Library), Histogram, Data Science, Machine Learning, Probability & Statistics, Linear Algebra, Regression Analysis

      Beginner · Course · 1 - 3 Months

    • R

      Rice University

      Python Data Analysis

      Skills you'll gain: Data Processing, Data Manipulation, Data Structures, Scripting, Data Import/Export, Scripting Languages, Data Analysis, Python Programming, Data Storage, Data Management

      4.7
      Rating, 4.7 out of 5 stars
      ·
      888 reviews

      Beginner · Course · 1 - 4 Weeks

    • R

      Rice University

      An Introduction to Interactive Programming in Python (Part 1)

      Skills you'll gain: Event-Driven Programming, Application Development, Interactive Design, Graphical Tools, User Interface (UI), Programming Principles, Computer Graphics, Python Programming, Program Development, Computer Programming, Simulations, Development Environment, Debugging, Arithmetic

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.3K reviews

      Mixed · Course · 1 - 3 Months

    NumPy learners also search

    Vision
    Computer Vision
    Image Analysis
    Beginner Computer Vision
    Computer Vision Projects
    Advanced Computer Vision
    Image Processing
    Image Classification
    1…567…18

    In summary, here are 10 of our most popular numpy courses

    • Applied Plotting, Charting & Data Representation in Python: University of Michigan
    • Deep Learning and Reinforcement Learning: IBM
    • Unsupervised Machine Learning: IBM
    • Python: de cero a analista de datos: Universidad Nacional Autónoma de México
    • Logistic Regression with NumPy and Python: Coursera Project Network
    • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
    • Python Data Visualization: Rice University
    • Python Object Basics: Functions, Recursion, and Objects: Codio
    • Python for Genomic Data Science: Johns Hopkins University
    • NumPy, Matplotlib & Pandas – Data Science Prerequisites: Packt

    Skills you can learn in Data Analysis

    Analytics (85)
    Big Data (64)
    Python Programming (47)
    Business Analytics (40)
    R Programming (37)
    Statistical Analysis (36)
    Sql (33)
    Data Model (29)
    Data Mining (27)
    Exploratory Data Analysis (26)
    Data Modeling (21)
    Data Manipulation (20)

    Frequently Asked Questions about Numpy

    NumPy is a powerful Python library used for mathematical and numerical computations. It stands for Numerical Python and is widely used in the field of data science, artificial intelligence, and machine learning. NumPy provides efficient handling of large multi-dimensional arrays and matrices, along with a collection of mathematical functions to perform operations on these arrays. It also offers tools for linear algebra, Fourier transform, random number generation, and integration with other programming languages like C/C++ and Fortran. By using NumPy, programmers can write code that is more concise and performant when dealing with numerical operations and data manipulation tasks.‎

    To work with NumPy, you need to learn the following skills:

    1. Python programming: Since NumPy is a library for Python, having a strong foundation in Python programming is essential.

    2. Array manipulation: NumPy is primarily used for working with arrays in Python. Therefore, understanding how to create, modify, and manipulate arrays is crucial.

    3. Data analysis and numerical computing: NumPy provides various functions and tools for performing calculations and numerical computations efficiently. Familiarity with data analysis concepts and numerical computing is necessary to make the most out of NumPy.

    4. Broadcasting: NumPy employs a concept called broadcasting, which allows the calculation of arrays with different shapes. Learning how broadcasting works in NumPy will enable you to operate on arrays effectively.

    5. Indexing and slicing: NumPy offers powerful indexing and slicing capabilities to access and manipulate data within arrays. Knowing how to index and slice arrays will help you extract specific elements or subsets of data efficiently.

    6. Linear algebra: NumPy provides robust linear algebra capabilities, including matrix operations, eigenvalue calculation, solving linear equations, and more. Having a fundamental understanding of linear algebra concepts will be beneficial when working with NumPy.

    7. Familiarity with NumPy functions: NumPy provides a vast number of functions tailored for various tasks like mathematical operations, statistical analysis, linear algebra computations, etc. Acquainting yourself with the most commonly used NumPy functions is essential to leverage the library effectively.

    By mastering these skills, you will be well-equipped to utilize NumPy effectively for numerical computations and data analysis using Python.‎

    With NumPy skills, you can pursue various job roles in industries such as data analysis, data science, machine learning, and scientific research. Some of the specific job titles you may be eligible for include:

    1. Data Analyst: Use NumPy to explore, analyze, and visualize data to help organizations make informed business decisions.

    2. Data Scientist: Apply NumPy along with other tools to conduct statistical analysis, build predictive models, and extract insights from large datasets.

    3. Machine Learning Engineer: Utilize NumPy to preprocess and manipulate data for training machine learning algorithms and developing predictive models.

    4. Research Scientist: Utilize NumPy for numerical computations and data manipulation in scientific research projects, such as analyzing experimental data or conducting simulations.

    5. Quantitative Analyst: Employ NumPy to develop mathematical models and algorithms for financial analysis, risk assessment, and investment strategies.

    6. Software Engineer: Apply NumPy along with other libraries to build efficient and scalable software solutions related to data analytics, machine learning, or scientific simulation.

    7. Academic Researcher: Utilize NumPy for data analysis and manipulation in various research fields, such as physics, biology, or engineering.

    8. Business Intelligence Analyst: Use NumPy to extract, process, and analyze data from multiple sources to provide insights and strategic recommendations to businesses.

    These are just a few examples, and the demand for NumPy skills is continuously growing in the industry. It's always recommended to explore job listings and requirements to get a better understanding of the opportunities available in your specific area of interest.‎

    People who are interested in data analysis, data science, or machine learning are best suited for studying NumPy. NumPy is a powerful library in Python that is widely used for numerical computing and data manipulation. It provides efficient and high-performance multidimensional array objects, along with a large collection of mathematical functions, making it an essential tool for working with large datasets and performing complex calculations. Therefore, individuals with a strong background or interest in these fields would benefit greatly from studying NumPy.‎

    There are several topics related to NumPy that you can study. Some of them include:

    1. Numerical Computing: NumPy is a fundamental library for numerical computing in Python. You can study various numerical computing concepts such as array operations, linear algebra, calculus, and statistical analysis.

    2. Data Analysis and Data Science: NumPy is extensively used in data analysis and data science workflows. You can explore topics like data manipulation, data visualization, and statistical modeling using NumPy arrays.

    3. Machine Learning: NumPy is an essential tool in machine learning algorithms. You can study topics like implementing regression, classification, clustering, and neural networks using NumPy arrays for data manipulation and calculations.

    4. Image Processing: NumPy provides excellent support for image processing tasks. You can learn about topics such as image filtering, edge detection, image enhancement, and more using NumPy arrays.

    5. Signal Processing: NumPy has a wide range of functions for signal processing. You can study topics like digital filters, Fourier analysis, digital signal processing techniques, and signal visualization using NumPy arrays.

    6. Computational Physics: NumPy is often used in computational physics to perform simulations and numerical calculations. You can study topics like numerical methods, solving differential equations, and modeling physical systems using NumPy arrays.

    7. Optimization: NumPy includes various optimization algorithms and tools. You can study topics like optimization techniques, mathematical programming, and solving optimization problems using NumPy arrays.

    These topics will provide you with a solid foundation in understanding and working with NumPy and its applications in various domains.‎

    Online NumPy courses offer a convenient and flexible way to enhance your knowledge or learn new NumPy is a powerful Python library used for mathematical and numerical computations. It stands for Numerical Python and is widely used in the field of data science, artificial intelligence, and machine learning. NumPy provides efficient handling of large multi-dimensional arrays and matrices, along with a collection of mathematical functions to perform operations on these arrays. It also offers tools for linear algebra, Fourier transform, random number generation, and integration with other programming languages like C/C++ and Fortran. By using NumPy, programmers can write code that is more concise and performant when dealing with numerical operations and data manipulation tasks. skills. Choose from a wide range of NumPy courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in NumPy, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

    Other topics to explore

    Arts and Humanities
    338 courses
    Business
    1095 courses
    Computer Science
    668 courses
    Data Science
    425 courses
    Information Technology
    145 courses
    Health
    471 courses
    Math and Logic
    70 courses
    Personal Development
    137 courses
    Physical Science and Engineering
    413 courses
    Social Sciences
    401 courses
    Language Learning
    150 courses

    Coursera Footer

    Technical Skills

    • ChatGPT
    • Coding
    • Computer Science
    • Cybersecurity
    • DevOps
    • Ethical Hacking
    • Generative AI
    • Java Programming
    • Python
    • Web Development

    Analytical Skills

    • Artificial Intelligence
    • Big Data
    • Business Analysis
    • Data Analytics
    • Data Science
    • Financial Modeling
    • Machine Learning
    • Microsoft Excel
    • Microsoft Power BI
    • SQL

    Business Skills

    • Accounting
    • Digital Marketing
    • E-commerce
    • Finance
    • Google
    • Graphic Design
    • IBM
    • Marketing
    • Project Management
    • Social Media Marketing

    Career Resources

    • Essential IT Certifications
    • High-Income Skills to Learn
    • How to Get a PMP Certification
    • How to Learn Artificial Intelligence
    • Popular Cybersecurity Certifications
    • Popular Data Analytics Certifications
    • What Does a Data Analyst Do?
    • Career Development Resources
    • Career Aptitude Test
    • Share your Coursera Learning Story

    Coursera

    • About
    • What We Offer
    • Leadership
    • Careers
    • Catalog
    • Coursera Plus
    • Professional Certificates
    • MasterTrack® Certificates
    • Degrees
    • For Enterprise
    • For Government
    • For Campus
    • Become a Partner
    • Social Impact
    • Free Courses
    • ECTS Credit Recommendations

    Community

    • Learners
    • Partners
    • Beta Testers
    • Blog
    • The Coursera Podcast
    • Tech Blog
    • Teaching Center

    More

    • Press
    • Investors
    • Terms
    • Privacy
    • Help
    • Accessibility
    • Contact
    • Articles
    • Directory
    • Affiliates
    • Modern Slavery Statement
    • Do Not Sell/Share
    Learn Anywhere
    Download on the App Store
    Get it on Google Play
    Logo of Certified B Corporation
    © 2025 Coursera Inc. All rights reserved.
    • Coursera Facebook
    • Coursera Linkedin
    • Coursera Twitter
    • Coursera YouTube
    • Coursera Instagram
    • Coursera TikTok