Published | 2022-05-20 |

Platform | Udemy |

Rating | 4.64 |

Number of Reviews | 10 |

Number of Students | 5024 |

Price | $19.99 |

Subjects |

*Improve your data science skills and solve over 330 exercises in Python, NumPy, Pandas and 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*arraysgenerating

*numpy*arraysgenerating

*numpy*arrays with random valuesiterating 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*classclassification, regression, clustering

discretization

feature extraction

*PolynomialFeatures*class*LabelEncoder*class*OneHotEncoder*class*StandardScaler*classdummy encoding

splitting data into train and test set

*LogisticRegression*classconfusion matrix

classification report

*LinearRegression*class*MAE*- Mean Absolute Error*MSE*- Mean Squared Error*sigmoid()*functionentorpy

accuracy score

*DecisionTreeClassifier*class*GridSearchCV*class*RandomForestClassifier*class*CountVectorizer*class*TfidfVectorizer*class*KMeans*class*AgglomerativeClustering*class*HierarchicalClustering*class*DBSCAN*classdimensionality reduction,

*PCA*analysisAssociation 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.