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

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

Python for Data Science - NumPy, Pandas & Scikit-Learn - 
Improve your data science skills and solve over 330 exercises in Python, NumPy, Pandas and Scikit-Learn!
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What you'll learn
  • solve over 330 exercises in NumPy, Pandas and Scikit-Learn
  • deal with real programming problems in data science
  • work with documentation and Stack Overflow
  • guaranteed instructor support

Requirements
  • basic knowledge of Python
  • basic knowledge of NumPy, Pandas and Scikit-Learn
Description
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

classification, regression, clustering

discretization

feature extraction

PolynomialFeatures class

LabelEncoder class

OneHotEncoder class

StandardScaler class

dummy encoding

splitting data into train and test set

LogisticRegression class

confusion matrix

classification report

LinearRegression class

MAE - Mean Absolute Error

MSE - Mean Squared Error

sigmoid() function

entorpy

accuracy score

DecisionTreeClassifier class

GridSearchCV class

RandomForestClassifier class

CountVectorizer class

TfidfVectorizer class

KMeans class

AgglomerativeClustering class

HierarchicalClustering class

DBSCAN class

dimensionality reduction, PCA analysis

Association Rules

LocalOutlierFactor class

IsolationForest class

KNeighborsClassifier class

MultinomialNB class

GradientBoostingRegressor class



This course is designed for people who have basic knowledge in Python, NumPy, Pandas and Scikit-Learn packages. It consists of 330 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.

Who this course is for:
  • everyone who wants to learn by doing
  • everyone who wants to improve Python programming skills
  • everyone who wants to improve data science skills
  • everyone who wants to prepare for an interview
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