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

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

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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 intervie