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

• 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