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

Python is one of the most popular programming languages in the world and has become the standard language for data science. Its simple syntax and extensive ecosystem make it ideal for analyzing data, automating tasks, and building machine learning solutions.

In data science, Python is commonly used alongside libraries such as:

  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-Learn

A Python program is built using variables, functions, and control structures.

Example:

name = "Sami"
print(name)

This creates a variable and prints its value. Python’s readability allows analysts to focus on solving problems rather than dealing with complicated syntax.

By mastering the basics, you’ll build a foundation for all future data science work.

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