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Data Sourcing
Installing Jupyter Notebook and Getting Started
Importing Data into Jupyter Notebook from a CSV File
Importing a Database into Jupyter Notebook
Using APIs to Import Data into Jupyter Notebook
Exploratory Data Analysis (EDA)
Data Collection and Cleaning
Using APIs as a Data Source
Outlier Detection and Treatment
Data Transformation for EDA
Data Normalization and Standardization
Data Integration
Data Visualization
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Descriptive Statistics
Measures of Central Tendency
Measures of Dispersion
Skewness and Kurtosis
Percentiles and Quantiles
Data Profiling
Summary Statistics
Data Types and Structures
Frequency Distribution
Value Counts
Data Transformation
Log Transformation
Square Root Transformation
Box-Cox Transformation
Z-score Normalization (Standardization)
Min-Max Scaling (Normalization)
Feature Engineering
Creating New Features
Feature Selection
Feature Extraction
Dimensionality Reduction
Correlation Analysis
Pearson Correlation
Spearman Rank Correlation
Kendall’s Tau
Chi-Square Test for Independence
Handling Categorical Data
One-Hot Encoding
Label Encoding
Frequency Encoding
Ordinal Encoding
Predictive Analytics and Machine Learning
Regression Algorithms
Linear Regression
Polynomial Regression
Ridge Regression
Lasso Regression
ElasticNet Regression
Random Forest Regression
Decision Tree Regression
Support Vector Regression (SVR)
Gradient Boosting Regression
Classification Algorithms
Logistic Regression
Decision Trees
Random Forest
Support Vector Machines (SVM)
k-Nearest Neighbors (KNN)
Naive Bayes
Neural Networks for Classification
Clustering Algorithms
K-Means Clustering
Hierarchical Clustering
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Mean Shift Clustering
Gaussian Mixture Models (GMM)
Dimensionality Reduction Algorithms
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Incremental Principal Component Analysis (IPCA)
Miscellaneous Topics
Time Series Analysis
Trend Analysis
Seasonality Analysis
Understanding Autocorrelation
Decomposition
Hypothesis Testing
T-tests
Analysis of Variance (ANOVA)
Chi-Square Tests
Mann-Whitney U Test
Wilcoxon Signed-Rank Test
Identifying Patterns and Trends in Data
Clustering Analysis
Anomaly Detection
Projects
Real Estate Price Predictions
Investment Funds of European Countries
Web Scraping Forbes Global 2000
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Home
Learn
Programming Languages
Python
R
SQL
Data Sourcing
Installing Jupyter Notebook and Getting Started
Importing Data into Jupyter Notebook from a CSV File
Importing a Database into Jupyter Notebook
Using APIs to Import Data into Jupyter Notebook
Exploratory Data Analysis (EDA)
Data Collection and Cleaning
Using APIs as a Data Source
Outlier Detection and Treatment
Data Transformation for EDA
Data Normalization and Standardization
Data Integration
Data Visualization
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Descriptive Statistics
Measures of Central Tendency
Measures of Dispersion
Skewness and Kurtosis
Percentiles and Quantiles
Data Profiling
Summary Statistics
Data Types and Structures
Frequency Distribution
Value Counts
Data Transformation
Log Transformation
Square Root Transformation
Box-Cox Transformation
Z-score Normalization (Standardization)
Min-Max Scaling (Normalization)
Feature Engineering
Creating New Features
Feature Selection
Feature Extraction
Dimensionality Reduction
Correlation Analysis
Pearson Correlation
Spearman Rank Correlation
Kendall’s Tau
Chi-Square Test for Independence
Handling Categorical Data
One-Hot Encoding
Label Encoding
Frequency Encoding
Ordinal Encoding
Predictive Analytics and Machine Learning
Regression Algorithms
Linear Regression
Polynomial Regression
Ridge Regression
Lasso Regression
ElasticNet Regression
Random Forest Regression
Decision Tree Regression
Support Vector Regression (SVR)
Gradient Boosting Regression
Classification Algorithms
Logistic Regression
Decision Trees
Random Forest
Support Vector Machines (SVM)
k-Nearest Neighbors (KNN)
Naive Bayes
Neural Networks for Classification
Clustering Algorithms
K-Means Clustering
Hierarchical Clustering
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Mean Shift Clustering
Gaussian Mixture Models (GMM)
Dimensionality Reduction Algorithms
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Incremental Principal Component Analysis (IPCA)
Miscellaneous Topics
Time Series Analysis
Trend Analysis
Seasonality Analysis
Understanding Autocorrelation
Decomposition
Hypothesis Testing
T-tests
Analysis of Variance (ANOVA)
Chi-Square Tests
Mann-Whitney U Test
Wilcoxon Signed-Rank Test
Identifying Patterns and Trends in Data
Clustering Analysis
Anomaly Detection
Projects
Real Estate Price Predictions
Investment Funds of European Countries
Web Scraping Forbes Global 2000
About
Contact
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