Mastering Data Analysis with Python Pandas

About Course

In this course, you will learn how to use Pandas, the leading Python library for data manipulation and analysis. The course will cover everything from basic data operations to advanced data wrangling, time series analysis, and data visualization. By the end of the course, you’ll be able to confidently use Pandas to perform data cleaning, transformation, and analysis on real-world datasets.

Course Content

Module 1: Introduction to Pandas and Setup
In the world of data science and analysis, Python has emerged as a powerful and versatile programming language. One of its most popular libraries for data manipulation and analysis is Pandas.

Introduction to Pandas and its Importance in Data Analysis

00:00
Installing Pandas and Setting Up the Environment

00:00
Introduction to Pandas Data Structures

00:00

Module 2: Data Structures in Pandas

Module 3: Data Wrangling

Module 4: Data Manipulation

Module 5: Advanced Data Analysis

Module 6: Data Visualization with Pandas

Module 7: Case Studies

Module 8: Capstone Project

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Mastering Data Analysis with Python Pandas

About Course

Course Content

Module 1: Introduction to Pandas and Setup
In the world of data science and analysis, Python has emerged as a powerful and versatile programming language. One of its most popular libraries for data manipulation and analysis is Pandas.

Introduction to Pandas and its Importance in Data Analysis

Installing Pandas and Setting Up the Environment

Introduction to Pandas Data Structures

Module 2: Data Structures in Pandas

Working with Series: Creation, Indexing, and Selection

Introduction to DataFrames

Indexing and Selecting Data from DataFrames

Module 3: Data Wrangling

Importing and Exporting Data with Pandas

Handling Missing Data

Data Filtering and Conditional Selection

String Operations in Pandas

Module 4: Data Manipulation

Data Sorting and Ranking

Adding, Modifying, and Deleting Columns

Working with Dates and Times

Merging, Joining, and Concatenating DataFrames

Module 5: Advanced Data Analysis

Grouping Data with GroupBy

Pivot Tables and Cross-Tabulation

Reshaping Data with Melt and Pivot

Module 6: Data Visualization with Pandas

Visualizing DataFrames using Pandas

Customizing Visualizations with Matplotlib and Seaborn

Module 7: Case Studies

Case Study 1: Analyzing a Real-World Dataset (e.g., a financial dataset)

Case Study 2: Cleaning and Visualizing Data from a CSV file

Case Study 3: Performing Time Series Analysis with Pandas

Module 8: Capstone Project

Capstone Project: Capstone Project: Perform end-to-end data cleaning, manipulation, and analysis on a complex dataset (e.g., sales data, stock prices, or public health data). Project includes importing data, cleaning it, performing analysis, and visualizing results.

About Course

Course Content

Module 1: Introduction to Pandas and Setup In the world of data science and analysis, Python has emerged as a powerful and versatile programming language. One of its most popular libraries for data manipulation and analysis is Pandas.

Introduction to Pandas and its Importance in Data Analysis

Installing Pandas and Setting Up the Environment

Introduction to Pandas Data Structures

Module 2: Data Structures in Pandas

Working with Series: Creation, Indexing, and Selection

Introduction to DataFrames

Indexing and Selecting Data from DataFrames

Module 3: Data Wrangling

Importing and Exporting Data with Pandas

Handling Missing Data

Data Filtering and Conditional Selection

String Operations in Pandas

Module 4: Data Manipulation

Data Sorting and Ranking

Adding, Modifying, and Deleting Columns

Working with Dates and Times

Merging, Joining, and Concatenating DataFrames

Module 5: Advanced Data Analysis

Grouping Data with GroupBy

Pivot Tables and Cross-Tabulation

Reshaping Data with Melt and Pivot

Module 6: Data Visualization with Pandas

Visualizing DataFrames using Pandas

Customizing Visualizations with Matplotlib and Seaborn

Module 7: Case Studies

Case Study 1: Analyzing a Real-World Dataset (e.g., a financial dataset)

Case Study 2: Cleaning and Visualizing Data from a CSV file

Case Study 3: Performing Time Series Analysis with Pandas

Module 8: Capstone Project

Capstone Project: Capstone Project: Perform end-to-end data cleaning, manipulation, and analysis on a complex dataset (e.g., sales data, stock prices, or public health data). Project includes importing data, cleaning it, performing analysis, and visualizing results.

Student Ratings & Reviews

Module 1: Introduction to Pandas and Setup
In the world of data science and analysis, Python has emerged as a powerful and versatile programming language. One of its most popular libraries for data manipulation and analysis is Pandas.