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

Matplotlib and Seaborn are popular visualization libraries for Python.

Example:

import matplotlib.pyplot as plt

plt.plot([1,2,3],[10,20,30])
plt.show()

Visualizations should be:

  • Clear
  • Accurate
  • Relevant
  • Easy to interpret

Poor visualization choices can mislead audiences and distort conclusions. Seaborn builds on top of Matplotlib and provides a higher-level interface for statistical graphics.

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