Course Content
Python for Data Analysis
Learn the Python fundamentals necessary for working with data and preparing datasets for analysis.
0/6
Introduction to Python
Working with Data Structures
Introduction to Pandas
Python Fundamentals
00:10:00
Pandas & Data Structures
00:10:00
Data Cleaning & Visualization
Learn how to prepare messy datasets and create visualizations that communicate meaningful insights.
0/6
Handling Missing Data
Data Visualization Fundamentals
Using Matplotlib and Seaborn
Data Cleaning
00:10:00
Visualization Assessment
00:10:00
Introduction to Machine Learning
Understand how machines learn from data and build your first predictive models.
0/6
What Is Machine Learning?
Supervised vs Unsupervised Learning
Building Your First Model
Machine Learning Basics
00:10:00
Model Building Assessment
00:10:00
Data Science Foundations

Pandas is the most widely used Python library for data analysis. It introduces two key objects:

Series

A single column of data.

DataFrame

A table of rows and columns.

Example:

import pandas as pd

data = {
    "Name": ["Alice", "Bob"],
    "Age": [25, 30]
}

df = pd.DataFrame(data)

DataFrames allow analysts to:

  • Filter data
  • Sort values
  • Perform calculations
  • Identify trends

Pandas forms the backbone of most data science workflows.

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