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

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Explore the NumPy Course Catalog

  • Status: New
    New
    P

    Packt

    Intro to NumPy

    Skills you'll gain: NumPy, Scientific Visualization, Jupyter, Time Series Analysis and Forecasting, Graphing, Data Visualization Software, Data Structures, Python Programming, Numerical Analysis, Data Manipulation, Mathematical Software

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    Python for Data Science, AI & Development

    Skills you'll gain: Data Import/Export, Programming Principles, Web Scraping, Python Programming, Jupyter, Data Structures, Data Processing, Pandas (Python Package), Data Manipulation, JSON, Computer Programming, Restful API, NumPy, Object Oriented Programming (OOP), Scripting, Application Programming Interface (API), Automation, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    42K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    NumPy and Pandas Basics for Future Data Scientists

    Skills you'll gain: Debugging, Data Analysis, Data Processing, Numerical Analysis, Critical Thinking

    4
    Rating, 4 out of 5 stars
    ·
    8 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    G

    Google

    Hello, Python!

    Skills you'll gain: Package and Software Management, Data Science, Computer Programming, Mathematical Software

    Beginner · Course · 1 - 4 Weeks

  • C

    Coursera Project Network

    Python for Data Analysis: Pandas & NumPy

    Skills you'll gain: Pandas (Python Package), NumPy, Data Analysis, Data Science, Python Programming, Data Structures, Exploratory Data Analysis, Data Manipulation, Computer Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    364 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free Trial
    Free Trial
    D

    Duke University

    Data Science with NumPy, Sets, and Dictionaries

    Skills you'll gain: NumPy, Data Structures, Data Analysis, Object Oriented Programming (OOP), Image Analysis, Data Science, Data Wrangling, Data Manipulation, Big Data, Performance Tuning, Python Programming

    3.9
    Rating, 3.9 out of 5 stars
    ·
    19 reviews

    Beginner · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    Data Analysis with Python

    Skills you'll gain: Exploratory Data Analysis, Data Wrangling, Data Transformation, Data Analysis, Data Cleansing, Data Manipulation, Data Import/Export, Predictive Modeling, Regression Analysis, Statistical Analysis, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data-Driven Decision-Making, Matplotlib, Feature Engineering, Data Visualization, Data Pipelines, NumPy, Python Programming

    4.7
    Rating, 4.7 out of 5 stars
    ·
    19K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    Get Started with Python

    Skills you'll gain: Object Oriented Programming (OOP), Data Structures, Python Programming, NumPy, Pandas (Python Package), Data Analysis, Scripting, Data Manipulation, Data Visualization, Algorithms, Debugging

    4.8
    Rating, 4.8 out of 5 stars
    ·
    1.7K reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    I

    IBM

    IBM Data Science

    Skills you'll gain: Exploratory Data Analysis, Data Wrangling, Dashboard, Data Visualization Software, Data Visualization, SQL, Unsupervised Learning, Plotly, Interactive Data Visualization, Peer Review, Supervised Learning, Data Transformation, Feature Engineering, Jupyter, Data Analysis, Data Cleansing, Data Literacy, Generative AI, Professional Networking, Data Import/Export

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    147K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    I

    IBM

    IBM Data Analyst

    Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Data Wrangling, Dashboard, Data Visualization Software, Plotly, Data Visualization, Generative AI, SQL, Interactive Data Visualization, Data Transformation, Data Analysis, Data Cleansing, Big Data, IBM Cognos Analytics, Excel Formulas, Professional Networking, Data Import/Export, Microsoft Excel, Python Programming

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    95K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    Duke University

    MLOps | Machine Learning Operations

    Skills you'll gain: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Application Deployment, Responsible AI, Data Manipulation, Exploratory Data Analysis, Containerization, Data Pipelines, CI/CD, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    515 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Machine Learning with Python

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Feature Engineering, Applied Machine Learning, Scikit Learn (Machine Learning Library), Machine Learning, Predictive Modeling, Dimensionality Reduction, Regression Analysis, Decision Tree Learning, Classification And Regression Tree (CART), Statistical Modeling

    4.7
    Rating, 4.7 out of 5 stars
    ·
    18K reviews

    Intermediate · Course · 1 - 3 Months

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

  • Intro to NumPy: Packt
  • Python for Data Science, AI & Development: IBM
  • NumPy and Pandas Basics for Future Data Scientists: University of Michigan
  • Hello, Python!: Google
  • Python for Data Analysis: Pandas & NumPy: Coursera Project Network
  • Data Science with NumPy, Sets, and Dictionaries: Duke University
  • Data Analysis with Python: IBM
  • Get Started with Python: Google
  • IBM Data Science: IBM
  • IBM Data Analyst: IBM

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.

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