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

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

Language: English
Created by: Paweł Krakowiak
Rate: 4.6 / 10  ratings
Enroll: 4,598 students

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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