# 100+ Exercises – Python Programming – Data Science – NumPy

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**RECOMMENDED LEARNING PATH**

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**PYTHON DEVELOPER:**

200+ Exercises – Programming in Python – from A to Z

210+ Exercises – Python Standard Libraries – from A to Z

150+ Exercises – Object Oriented Programming in Python – OOP

150+ Exercises – Data Structures in Python – Hands-On

100+ Exercises – Advanced Python Programming

100+ Exercises – Unit tests in Python – unittest framework

**SQL DEVELOPER:**

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**COURSE DESCRIPTION**

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**100+ Exercises – Python Programming – Data Science – NumPy**

Welcome to the course **100+ Exercises – Python Programming – Data Science – NumPy**, where you can test your Python programming skills in data science, specifically in NumPy.

Some topics you will find in the exercises:

working with

*numpy*arraysgenerating

*numpy*arraysgenerating

*numpy*arrays with random valuesiterating through arrays

dealing with missing values

working with

*matrices*reading/writing files

joining arrays

reshaping arrays

computing basic array statistics

sorting arrays

filtering arrays

image as an array

linear algebra

matrix multiplication

determinant of the matrix

eigenvalues and eignevectors

inverse matrix

shuffling arrays

working with polynomials

working with dates

working with strings in array

solving systems of equations

The course is designed for people who have basic knowledge in Python and *NumPy *package. It consists of **100 exercises with solutions**.

This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.

If you’re wondering if it’s worth taking a step towards Python, don’t hesitate any longer and take the challenge today.

Author : **Paweł Krakowiak**

Ratings : **4.1 / 5.0**

Students : **29,572 students**

https://www.udemy.com/100-exercises-python-programming-data-science-numpy/?couponCode=FREEDROP

Category : Development, Data Science, NumPy