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

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

• 100+ Exercises – Python Programming – Data Science – Pandas

• 100+ Exercises – Python – Data Science – scikit-learn

• 250+ Exercises – Data Science Bootcamp in Python

SQL DEVELOPER:

• SQL Bootcamp – Hands-On Exercises – SQLite – Part I

• SQL Bootcamp – Hands-On Exercises – SQLite – Part II

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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 arrays
• generating numpy arrays
• generating numpy arrays with random values
• iterating 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.