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Pandas Statistical Analysis

Turn data into insights with Pandas statistical superpowers!

4.5
Advance

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Pandas Statistical Analysis
8 Modules
March 2026
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Course Curriculum

8 Modules · 9 Chapters · 27 Topics · 160 Sub-topics

01
Foundation
2 Chapters · 6 Topics · 27 Sub-topics
Getting Started with Pandas
2 Topics
Introduction to Pandas
5 Sub-topics
What is Pandas and Why Use It
Installing Pandas and Verifying Installation
Understanding Pandas Data Structures Overview
Importing Pandas and Basic Conventions
Your First Pandas Program
Pandas Development Environment
4 Sub-topics
Setting Up Jupyter Notebook for Pandas
Using VS Code with Pandas
Understanding Pandas Documentation
Exploring Pandas Community Resources
DataFrame Fundamentals
4 Topics
Creating DataFrames
5 Sub-topics
Creating DataFrame from Dictionary of Lists
Creating DataFrame from List of Dictionaries
Creating DataFrame from NumPy Arrays
Creating DataFrame from Series
DataFrame Attributes - shape, size, columns, index, dtypes
DataFrame Inspection
3 Sub-topics
Viewing DataFrame - head(), tail(), sample()
DataFrame Info and Memory Usage
Understanding DataFrame Structure
DataFrame Access and Selection
6 Sub-topics
Accessing Columns
Accessing Rows with loc
Accessing Rows with iloc
Selecting Multiple Columns
Selecting Rows and Columns Together
Boolean Indexing in DataFrame
DataFrame Modification
4 Sub-topics
Adding New Columns
Deleting Columns
Renaming Columns and Index
Modifying DataFrame Values
02
Descriptive Statistics
1 Chapters · 3 Topics · 19 Sub-topics
Data Exploration and Inspection
3 Topics
Statistical Summaries
5 Sub-topics
Understanding describe() for Statistical Summary
Using info() for DataFrame Overview
Checking Unique Values
Value Counts for Frequency Distribution
Correlation Analysis
Data Quality Assessment
5 Sub-topics
Finding Null Values
Data Type Inspection
Checking Duplicates
Memory Usage Analysis
Exploring Categorical Data
Data Profiling
9 Sub-topics
Comprehensive Summary Statistics
Distribution Analysis
Skewness and Kurtosis
Percentile Calculations
Value Counts and Frequencies
Unique Values Analysis
Data Cardinality
Creating Data Profiles
Automated Profiling Reports
03
Data Profiling
1 Chapters · 3 Topics · 19 Sub-topics
Data Exploration and Inspection
3 Topics
Statistical Summaries
5 Sub-topics
Understanding describe() for Statistical Summary
Using info() for DataFrame Overview
Checking Unique Values
Value Counts for Frequency Distribution
Correlation Analysis
Data Quality Assessment
5 Sub-topics
Finding Null Values
Data Type Inspection
Checking Duplicates
Memory Usage Analysis
Exploring Categorical Data
Data Profiling
9 Sub-topics
Comprehensive Summary Statistics
Distribution Analysis
Skewness and Kurtosis
Percentile Calculations
Value Counts and Frequencies
Unique Values Analysis
Data Cardinality
Creating Data Profiles
Automated Profiling Reports
04
Correlation Analysis
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules
05
Window Functions
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules
06
Statistical Testing Preparation
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules
07
Distribution Analysis
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules
08
Outlier Analysis
1 Chapters · 3 Topics · 19 Sub-topics
Statistical Operations
3 Topics
Window Functions
7 Sub-topics
Rolling Mean and Sum
Rolling Min and Max
Rolling Standard Deviation
Expanding Windows
Exponentially Weighted Moving Average
Window Aggregations
Custom Window Functions
Statistical Measures
4 Sub-topics
Correlation and Covariance
Quantile Calculations
Cumulative Operations
Statistical Analysis Methods
Outlier Detection
8 Sub-topics
Detecting Outliers with Z-Score
Detecting Outliers with IQR
Detecting Outliers with Percentiles
Visualizing Outliers Concept
Handling Outliers - Removal
Handling Outliers - Capping
Handling Outliers - Transformation
Domain-Specific Outlier Rules

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