Python for Data Science – NumPy, Pandas & Scikit-Learn

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

Some topics you will find in the NumPy 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 topics you will find in the Pandas 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

Topics you will find in the Scikit–Learn exercises:

preparing data to machine learning models

working with missing values, SimpleImputer class