Udemy

Master NumPy Basics For Data Science & Data Analysis

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  • 114,966 Students
  • Updated 10/2025
4.1
(885 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
1 Hour(s) 56 Minute(s)
Language
English
Taught by
Pruthviraja L
Rating
4.1
(885 Ratings)
1 views

Course Overview

Master NumPy Basics For Data Science & Data Analysis

Master The Basics of NumPy Which Will Help You Achieve Data Science & Data Analysis Skills As A Beginner

Hi, welcome to the 'Master NumPy Basics For Data Science & Data Analysis' course. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations. We know that the matrix and arrays play an important role in numerical computation and data analysis. Pandas and other Machine Learning or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and Machine Learning packages can reduce the time and improve the performance of the data computation. NumPy based arrays are 10 to 100 times (even more than 100 times) faster than the Python Lists, hence if you are planning to work as a Data Analyst or Data Scientist or Big Data Engineer with Python, then you must be familiar with the NumPy as it offers a more convenient way to work with Matrix-like objects like Nd-arrays. And also we’re going to do a demo where we prove that using a Numpy vectorized operation is faster than normal Python lists.


So if you want to learn about the fastest python-based numerical multidimensional data processing framework, which is the foundation for many data science packages like pandas for data analysis, sklearn & scikit-learn for machine learning algorithm, you are at the right place and right track. The course contents are listed in the "Course content" section of the course, please go through it.

I wish you all the very best and good luck with your future endeavors. Looking forward to seeing you inside the course.

Towards your success:

Pruthviraja L

Course Content

  • 6 section(s)
  • 17 lecture(s)
  • Section 1 Introduction - Installation and Setup
  • Section 2 NumPy Basics
  • Section 3 Indexing In NumPy
  • Section 4 File Handling In NumPy
  • Section 5 Numerical Computation in NumPy
  • Section 6 Thank You and Bonus Section

What You’ll Learn

  • NumPy For Data Analysis
  • NumPy For Data Science
  • Numerical Computation Using Python
  • How To Work With N-Dimensional Arrays
  • How To Perform Matrix Computation


Reviews

  • R
    Rahul Verma
    4.5

    good

  • A
    Ahmed
    2.5

    Audio is so bad, but the content is solid

  • A
    ARABINDA ALIPATRA
    5.0

    very good experience here

  • S
    Sai Sushma Avula
    5.0

    yes it is good

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