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NumPy For Data Analysis & Machine Learning

NumPy For Data Analysis & Machine Learning

welcome to the 'NumPy For Data Science & Machine Learning' course. This forms the basis for everything else.

What you'll learn

  • To handle or create single- or multi- dimensional arrays
  • High-performance multidimensional array object, and tools for working with these arrays
  • Scientific computing with Python
  • Aggregation of two or more arrays
  • Mathematical operations on arrays


  • Anaconda Installation to work with the NumPy and Python
  • Basic mathematics
  • If students knows Python, that is well & good
  • Willing to learn data analysis, data science or numerical computation for programm


Hi, welcome to the 'NumPy For Analysis & Machine Learning 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 ML or AI tools need tabular or array-like data to work efficiently, so using NumPy in Pandas and ML 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 the 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.

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