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.