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    • Numpy

    NumPy Courses Online

    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: Free
      Free
      P

      Pontificia Universidad Católica de Chile

      Introducción a la programación en Python I: Aprendiendo a programar con Python

      Skills you'll gain: Python Programming, Data Structures, Computer Programming, Program Development, Application Development, Algorithms, File Management

      4.4
      Rating, 4.4 out of 5 stars
      ·
      4.8K reviews

      Beginner · Course · 1 - 3 Months

    • D

      Duke University

      Data Visualization with Python

      Skills you'll gain: Data Visualization Software, Data Visualization, Data Storytelling, Interactive Data Visualization, Plotly, Matplotlib, Dashboard, Seaborn, Tableau Software, Scatter Plots, Histogram, Google Sheets, Microsoft Excel, Pandas (Python Package), Data Manipulation

      4.1
      Rating, 4.1 out of 5 stars
      ·
      24 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      N

      Nanjing University

      用Python玩转数据 Data Processing Using Python

      Skills you'll gain: Object Oriented Programming (OOP), Data Structures, Statistical Analysis, Data Mining, Data Analysis, Data Processing, Pandas (Python Package), NumPy, Web Scraping, Python Programming, Data Manipulation, Programming Principles, User Interface (UI) Design

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.6K reviews

      Mixed · Course · 1 - 3 Months

    • I

      IBM

      Applied Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Plotly, Data Wrangling, Data Visualization, Interactive Data Visualization, Exploratory Data Analysis, Data Cleansing, Jupyter, Matplotlib, Data Analysis, Pandas (Python Package), Data Manipulation, Seaborn, Data Import/Export, Predictive Modeling, Web Scraping, Automation, Data Science, Python Programming

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      58K reviews

      Beginner · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      Principal Component Analysis with NumPy

      Skills you'll gain: Exploratory Data Analysis, NumPy, Data Visualization, Data Analysis, Seaborn, Matplotlib, Cloud Computing, Jupyter, Dimensionality Reduction, Unsupervised Learning, Applied Machine Learning, Python Programming, Linear Algebra

      4.6
      Rating, 4.6 out of 5 stars
      ·
      295 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • I

      IBM

      IBM Data Analyst Capstone Project

      Skills you'll gain: Dashboard, Exploratory Data Analysis, Data Wrangling, Statistical Analysis, Data Cleansing, IBM Cognos Analytics, Data Manipulation, Data Collection, Data Presentation, Data Analysis, Web Scraping, Data Storytelling, Box Plots, Pandas (Python Package), Scatter Plots, Histogram

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.3K reviews

      Advanced · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Python for Data Visualization: Matplotlib & Seaborn

      Skills you'll gain: Matplotlib, Histogram, Plot (Graphics), Data Visualization, Seaborn, Scatter Plots, Data Visualization Software, Statistical Visualization, Graphing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      67 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • I

      Imperial College London

      Mathematics for Machine Learning: PCA

      Skills you'll gain: Dimensionality Reduction, NumPy, Probability & Statistics, Feature Engineering, Jupyter, Data Science, Statistics, Linear Algebra, Python Programming, Advanced Mathematics, Machine Learning, Calculus

      4
      Rating, 4 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Linear Regression with Python

      Skills you'll gain: Regression Analysis, NumPy, Applied Machine Learning, Supervised Learning, Machine Learning, Predictive Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      428 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Python for Data Visualization:Matplotlib & Seaborn(Enhanced)

      Skills you'll gain: Matplotlib, Seaborn, Plot (Graphics), Scatter Plots, Histogram, Data Visualization, Data Visualization Software, Statistical Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      25 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • D
      S

      Multiple educators

      Machine Learning

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Scikit Learn (Machine Learning Library), Artificial Intelligence, NumPy, Predictive Modeling, Deep Learning, Reinforcement Learning, Random Forest Algorithm, Feature Engineering

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      34K reviews

      Beginner · Specialization · 1 - 3 Months

    • I

      IBM

      Data Visualization with Python

      Skills you'll gain: Data Visualization Software, Plotly, Data Visualization, Dashboard, Interactive Data Visualization, Matplotlib, Seaborn, Data Presentation, Data Analysis, Geospatial Information and Technology, Pandas (Python Package), Scatter Plots, Histogram, Heat Maps, Box Plots

      4.5
      Rating, 4.5 out of 5 stars
      ·
      12K reviews

      Intermediate · Course · 1 - 3 Months

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

    • Introducción a la programación en Python I: Aprendiendo a programar con Python: Pontificia Universidad Católica de Chile
    • Data Visualization with Python: Duke University
    • 用Python玩转数据 Data Processing Using Python: Nanjing University
    • Applied Data Science: IBM
    • Principal Component Analysis with NumPy: Coursera Project Network
    • IBM Data Analyst Capstone Project: IBM
    • Python for Data Visualization: Matplotlib & Seaborn: Coursera Project Network
    • Mathematics for Machine Learning: PCA: Imperial College London
    • Linear Regression with Python: Coursera Project Network
    • Python for Data Visualization:Matplotlib & Seaborn(Enhanced): Coursera Project Network

    Skills you can learn in Data Analysis

    Analytics (85)
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    Python Programming (47)
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    R Programming (37)
    Statistical Analysis (36)
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    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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