Python Data Visualization: Matplotlib Bootcamp A-Z [2025]

Master Matplotlib to plot figures for data analytics & business intelligence + 50 exercises [2025] (Bootcamp)

What you'll learn

Create clear, customized line plots with different styles.

Design scatter plots with size, color, and marker variations.

Build bar charts, including stacked and grouped formats.

Develop histograms with custom bins and density curves.

Construct pie charts with exploded slices and rotation.

Generate heatmaps for analyzing correlations.

Implement box plots to display distributions.

Create 3D plots, including scatter and line visualizations.

Analyze time series data with moving averages.

Enhance plot aesthetics with annotations and transparency.

Requirements

No requirements are needed, we teach you EVERYTHING even basics of Python

Description

Data visualization is an essential skill in today’s data-driven world. Whether you are a data scientist, engineer, researcher, or analyst, the ability to present data effectively can make a significant difference in your work. This comprehensive course will take you on a step-by-step journey through the fundamentals and advanced capabilities of Matplotlib, the most widely used Python library for plotting and visualization.With over 50 hands-on coding exercises, this course covers everything from basic line plots to advanced multi-dimensional visualizations, ensuring that you develop a strong practical understanding of how to create, customize, and enhance visual representations of data. You will learn to produce clear, professional, and insightful plots that can be used for presentations, reports, publications, and business analysis.What This Course Covers:* Python Fundamentals for Data VisualizationBefore diving into Matplotlib, we provide essential Python programming concepts to ensure all learners—beginners and experienced coders alike—can follow along with ease.* Mastering Line PlotsLearn how to create solid, dashed, and dotted line plots.Customize marker styles, colors, edge widths, and labels.Work with multiple line plots and adjust line thickness, transparency, and styling.*  Advanced Scatter Plot TechniquesMaster scatter plots with various marker sizes, colors, and annotations.Explore randomized scatter plots and use color bars for added data insights.Learn how to handle large datasets efficiently and highlight important data points.* Bar Charts & Histograms for Data AnalysisDevelop vertical, horizontal, grouped, and stacked bar charts.Enhance bar charts with error bars, text annotations, and logarithmic scaling.Construct custom histograms, modify bin sizes, and overlay density plots for better data interpretation.* Pie Charts & Donut ChartsCreate professional pie charts with rotation, exploded slices, and multi-chart figures.Learn how to optimize pie chart readability for presentations and reports.* Advanced Plotting with Box Plots, Heatmaps, and 3D VisualizationsImplement box plots with custom notches and mean markers to analyze distributions.Generate heatmaps to visualize data correlations effectively.Explore 3D scatter plots and line plots to represent multi-dimensional data.* Time Series & Trend AnalysisLearn how to plot time series data, detect trends, peaks, and moving averages.Apply visualization techniques to business, finance, and scientific research data.Why This Course is Unique?* Practical and Hands-On Approach: With over 50+ interactive coding exercises, you will develop real-world visualization skills rather than just theoretical knowledge.* Beginner to Advanced Coverage: Whether you are a beginner looking to build a foundation or a professional aiming to master advanced Matplotlib techniques, this course caters to all levels.* Comprehensive Customization Techniques: Learn color mapping, annotation, transparency settings, font adjustments, and multi-chart layouts to make your plots publication-ready.* Who Can Benefit?Students seeking to build their programming and data visualization skills.Data analysts, engineers, and business professionals looking to enhance their reporting abilities.Researchers needing scientific-quality figures for journals and conference papers.Anyone who wants to transform raw data into meaningful visual insights.By the end of this course, you will confidently create, customize, and optimize visualizations, making your data more insightful, compelling, and easy to interpret.

Overview

Section 1: Introduction

Lecture 1 Coding environment

Lecture 2 How to navigate this course?

Section 2: Python Basics

Lecture 3 Numeric data types

Lecture 4 Strings

Lecture 5 To read input

Lecture 6 Mathematical operations

Lecture 7 Mathematical operations 2

Lecture 8 Mathematical operations 3

Lecture 9 Booleans 1

Lecture 10 Booleans 2

Lecture 11 Booleans 3

Lecture 12 Booleans 4

Lecture 13 Booleans 5

Lecture 14 Booleans 6

Lecture 15 Strings 1

Lecture 16 Strings 2

Lecture 17 Strings 3

Lecture 18 Math modules 1

Lecture 19 Math modules 2

Lecture 20 Math modules 3

Lecture 21 Math modules 4

Lecture 22 Math modules 5

Lecture 23 Math modules 6

Lecture 24 Math modules 7

Lecture 25 Loops 1

Lecture 26 Loops 2

Lecture 27 Loops 3

Lecture 28 Loops 4

Section 3: Further coding exercises of Python basics

Lecture 29 Coding exercise

Lecture 30 Coding exercise

Lecture 31 Coding exercise

Lecture 32 Coding exercise

Lecture 33 Coding exercise

Lecture 34 Coding exercise

Section 4: Basic Plotting with Matplotlib

Lecture 35 Coding exercise #1: Line plots - with solid lines

Lecture 36 Coding exercise #2: Line plots - with dashed and dotted lines

Lecture 37 Coding exercise #3: Line plots - with different markers and line styles

Lecture 38 Coding exercise #4: Line plots - customized

Lecture 39 Coding exercise #5: Line plots - multiple line customization

Lecture 40 Coding exercise #6: Line plots - width customization

Lecture 41 Coding exercise #7: Line plots - Marker size, edge width and edge color

Lecture 42 Coding exercise #8: Line plots - RGB and Hexadecimal colors

Lecture 43 Coding exercise #9: Line plots - Transparency adjustment

Lecture 44 Coding exercise #10: Scatter plots - Basics

Lecture 45 Coding exercise #11: Scatter plots - modify figure size

Lecture 46 Coding exercise #12: Scatter plots - random sizes

Lecture 47 Coding exercise #13: Scatter plots - random color and color bar

Lecture 48 Coding exercise #14: Scatter plots - font sizes and more customization

Lecture 49 Coding exercise #15: Scatter plots - annonation and labling

Lecture 50 Coding exercise #16: Handling large datasets

Lecture 51 Coding exercise #17: Scatter plots - highlighted points

Lecture 52 Coding exercise #18: Scatter plots - two scatter plots in one figure

Lecture 53 Coding exercise #19: Scatter plots - 3D

Lecture 54 Coding exercise #20: Bar charts

Lecture 55 Coding exercise #21: Bar charts - customization of bar charts

Lecture 56 Coding exercise #22: Bar charts - text annotation

Lecture 57 Coding exercise #23: Bar charts - horizontal bar charts

Lecture 58 Coding exercise #24: Bar charts - grouped bar charts

Lecture 59 Coding exercise #25: Bar charts - stacked bar charts

Lecture 60 Coding execise #26: Bar charts - error bars

Lecture 61 Coding execise #27: Bar charts - logarithmic scale

Lecture 62 Coding execise #28: Histograms

Lecture 63 Coding execise #29: Histograms - customized histogram

Lecture 64 Coding execise #30: Histograms - adjusting number of bins

Lecture 65 Coding execise #31: Histograms - normalized histogram (density plot)

Lecture 66 Coding execise #32: Histograms - Kernel Density Estimation curve

Lecture 67 Coding execise #33: Histograms - cumulative

Lecture 68 Coding execise #34: Histograms - Horizontal histograms

Lecture 69 Coding execise #35: Histograms - logarithmic scale

Lecture 70 Coding exercise #36: Pie charts

Lecture 71 Coding exercise #37: Pie charts - customized

Lecture 72 Coding exercise #38: Pie charts - exploded slice

Lecture 73 Coding exercise #39: Pie charts - rotation

Lecture 74 Coding exercise #40: Pie charts - donut chart

Lecture 75 Coding exercise #41: Pie charts - two pie charts in one figure

Lecture 76 Coding exercise #42: Review - three plots in one figure

Lecture 77 Coding exercise #43: Review - some insights

Section 5: Advanced Plot Types in Matplotlib

Lecture 78 Box plots

Lecture 79 Coding exercise #44: Box plots

Lecture 80 Coding exercise #45: Box plots - customized with notch

Lecture 81 Coding exercise #46: Box plots - Multiple box plots

Lecture 82 Coding exercise #47: Box plots - horizontal with mean marker

Lecture 83 Heatmaps

Lecture 84 Coding exercise #48: Heatmaps - basics

Lecture 85 Coding exercise #49: Heatmaps - correlated

Lecture 86 Coding exercise #50: Heatmaps - custom labels

Lecture 87 Coding exercise #51: Heatmaps - normalized

Lecture 88 Coding exercise #52: Heatmaps - masking

Lecture 89 Coding exercise #53: 3D plots- scatter plot

Lecture 90 Coding exercise #54: 3D plots- line plot

Lecture 91 Coding exercise #55: Time series plots

Lecture 92 Coding exercise #56: Time series plots - moving average

Lecture 93 Coding exercise #57: Time series plots - peaks and mins

Students seeking to build their programming and data visualization skills.,Data analysts, engineers, and business professionals looking to enhance their reporting abilities.,Researchers needing scientific-quality figures for journals and conference papers.,Anyone who wants to transform raw data into meaningful visual insights.