Python for Data Science - NumPy, Pandas & Scikit-Learn
Python for Data Science - NumPy, Pandas & Scikit-Learn - Improve your data science skills and solve over 330 exercises in Python, NumPy, Pandas and Scikit-Learn!
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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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