Data Visualization with Python for Beginners

A practical, no-frills introduction to Matplotlib that helps beginners create polished visualizations quickly using Python.

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access	Lifetime
level	Beginner
certificate	Certificate of completion
language	English

#Udemy

Description
Additional information

What will you learn in Data Visualization with Python for Beginners Course

Create line plots, scatter plots, and 1D/2D histograms using Matplotlib
Customize plots with colors, line styles, titles, axis labels, legends, and text annotations

Adjust axis scales (e.g. log scale), ticks, figure size, and styles
Add error bars, work with intensity colour maps, embed images into plots, and create 3D plots

Program Overview

Module: Matplotlib Essentials & Visualization Techniques

⏳ ~3.5 hours (total course length)

Topics: Basic plotting, figure setup, scatter & line charts, histograms, customizing plots, annotations, legends, axis manipulation, error bars, log scaling, and saving figures.
Hands‑on: Step-by-step notebooks and live coding demonstrations where learners build and customize visualizations using sample datasets, primarily via Jupyter Notebook.

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Job Outlook

Highly relevant for roles in Data Analysis, Data Science, Business Intelligence, and Research, where effective visual communication is key.
Strong visualization skills with Matplotlib distinguish professionals by enabling clear data storytelling and story-driven charts.
Python-based visualization proficiency is advantageous in sectors like fintech, healthcare analytics, e‑commerce, and academia.
Helps build foundational visualization techniques for progression to tools like Seaborn, Plotly, or dashboard frameworks.

9.7Expert Score

Highly Recommendedx

This course offers a focused and approachable introduction to Matplotlib for beginners. With clear lectures and practical coding examples, it covers all the core functionality needed to start creating publication-quality graphs from real data.

Value

Price

9.2

Skills

9.4

Information

9.5

PROS

Covers essential Matplotlib features with clear, code-backed examples.
Ideal for beginners looking to make charts from scratch in Python environments.
Includes common plot customizations like annotations, log scaling, and figure export.

CONS

Last updated in April 2022 – may not include the latest Matplotlib API improvements.
No coverage of interactive plotting libraries (e.g., Plotly) or modern statistical plotting (Seaborn).