Double pack! Improve your Python programming and data science skills and solve over 230 exercises in NumPy and Pandas!
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.

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!”
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