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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

    • C

      Coursera Project Network

      Master Data Analysis with Pandas: Learning Path 1 (Enhanced)

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

      4.6
      Rating, 4.6 out of 5 stars
      ·
      25 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free
      Free
      N

      Northeastern University

      Data Visualization with Python & R for Engineers

      Skills you'll gain: Data Storytelling, Statistical Visualization, Data-Driven Decision-Making, Data Visualization Software, Data Mining, Visualization (Computer Graphics), Exploratory Data Analysis, Data Cleansing, Data Analysis, Data Manipulation, Big Data, Programming Principles, Python Programming

      Mixed · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Perform exploratory data analysis on retail data with Python

      Skills you'll gain: Data-Driven Decision-Making, Business Analytics, Data Analysis, Data Cleansing, Statistical Analysis, Exploratory Data Analysis, Data Manipulation, Customer Analysis, Trend Analysis, Pandas (Python Package), Python Programming

      4.5
      Rating, 4.5 out of 5 stars
      ·
      15 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Análisis Exploratorio de Datos con Python

      Skills you'll gain: Exploratory Data Analysis, Scatter Plots, Data Analysis, Correlation Analysis, Box Plots, Pandas (Python Package), Matplotlib, Data Visualization Software, Histogram, Statistical Analysis, Python Programming

      Intermediate · Guided Project · Less Than 2 Hours

    • C

      Coursera Project Network

      Use Python for Non-Data Role

      Skills you'll gain: Jupyter, Microsoft Excel, Spreadsheet Software, Project Schedules, Data Import/Export, Data Visualization Software, Python Programming, Data Structures, Data Manipulation, Business Correspondence

      Intermediate · Guided Project · Less Than 2 Hours

    • K

      Korea Advanced Institute of Science and Technology(KAIST)

      Practical Python for AI Coding 2

      Skills you'll gain: Matplotlib, Data Visualization, Tensorflow, NumPy, Pandas (Python Package), Object Oriented Programming (OOP), Seaborn, Python Programming, Keras (Neural Network Library), Artificial Intelligence, Scikit Learn (Machine Learning Library), Data Manipulation, Development Environment

      4.6
      Rating, 4.6 out of 5 stars
      ·
      25 reviews

      Beginner · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      신경망 및 딥 러닝

      Skills you'll gain: Deep Learning, Artificial Neural Networks, Supervised Learning, Artificial Intelligence, Computer Vision, Network Architecture, Machine Learning, Performance Tuning, Linear Algebra, Calculus

      5
      Rating, 5 out of 5 stars
      ·
      6 reviews

      Intermediate · Course · 1 - 4 Weeks

    • P

      Packt

      Natural Language Processing with Real-World Projects

      Skills you'll gain: Matplotlib, Pandas (Python Package), Data Visualization, Natural Language Processing, NumPy, Linear Algebra, Deep Learning, Semantic Web, Data Manipulation, Machine Learning Algorithms, Machine Learning, Supervised Learning, Text Mining, Data Processing, Machine Learning Methods, Unstructured Data, Applied Machine Learning, Markov Model, Dimensionality Reduction, Python Programming

      Beginner · Specialization · 3 - 6 Months

    • P

      Packt

      Deep Learning with Real-World Projects

      Skills you'll gain: Matplotlib, Deep Learning, Linear Algebra, Artificial Neural Networks, NumPy, Image Analysis, Keras (Neural Network Library), Data Visualization Software, Seaborn, Pandas (Python Package), Tensorflow, Machine Learning, Applied Machine Learning, Computer Vision, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Data Analysis, PyTorch (Machine Learning Library), Jupyter, Python Programming

      Beginner · Specialization · 3 - 6 Months

    • P

      Packt

      Fundamentals of AI, Machine Learning, and Python Programming

      Skills you'll gain: PyTorch (Machine Learning Library), Deep Learning, Matplotlib, Artificial Intelligence, Pandas (Python Package), Python Programming, NumPy, Data Analysis, Artificial Neural Networks, Tensorflow, Exploratory Data Analysis, Data Visualization, Classification And Regression Tree (CART), Data Processing, Regression Analysis, Data Science, Machine Learning

      Beginner · Course · 3 - 6 Months

    • Status: New
      New
      U

      University of Colorado Boulder

      BiteSize Python for Absolute Beginners: Data Structures

      Skills you'll gain: Data Structures, Python Programming, Programming Principles, Data Management

      Beginner · Course · 1 - 3 Months

    • U

      University of London

      Statistics and Clustering in Python

      Skills you'll gain: Pandas (Python Package), NumPy, Probability & Statistics, Unsupervised Learning, Data Science, Statistics, Data Analysis, Statistical Analysis, Jupyter, Machine Learning Algorithms, Data Manipulation, Descriptive Statistics, Matplotlib, Data Visualization Software, Python Programming

      4.4
      Rating, 4.4 out of 5 stars
      ·
      11 reviews

      Beginner · Course · 1 - 4 Weeks

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

    • Master Data Analysis with Pandas: Learning Path 1 (Enhanced): Coursera Project Network
    • Data Visualization with Python & R for Engineers: Northeastern University
    • Perform exploratory data analysis on retail data with Python: Coursera Project Network
    • Análisis Exploratorio de Datos con Python: Coursera Project Network
    • Use Python for Non-Data Role: Coursera Project Network
    • Practical Python for AI Coding 2: Korea Advanced Institute of Science and Technology(KAIST)
    • 신경망 및 딥 러닝: DeepLearning.AI
    • Natural Language Processing with Real-World Projects: Packt
    • Deep Learning with Real-World Projects: Packt
    • Fundamentals of AI, Machine Learning, and Python Programming: 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.

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