230+ Exercises – Python for Data Science – NumPy + Pandas

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230+ Exercises – Python for Data Science – NumPy + Pandas, Double pack! Improve your Python programming and data science skills and solve over 230 exercises in NumPy and Pandas!

Course Description

Welcome to the 230+ Exercises – Python for Data Science – NumPy + Pandas course where you can test your Python programming skills in data science, specifically in NumPy and Pandas.

Some numpy 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

Some pandas topics you will find in the exercises:

  • working with Series
  • working with DatetimeIndex
  • working with DataFrames
  • reading/writing files
  • working with different data types in DataFrames
  • working with indexes
  • working with missing values
  • filtering data
  • sorting data
  • grouping data
  • mapping columns
  • computing correlation
  • concatenating DataFrames
  • calculating cumulative statistics
  • working with duplicate values
  • preparing data to machine learning models
  • dummy encoding
  • working with csv and json filles
  • merging DataFrames
  • pivot tables

The course is designed for people who have basic knowledge in Python, NumPy and Pandas. It consists of 230 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.


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