Python Data Visualization Mastery: From Beginner to Expert

Name: Python Data Visualization Mastery: From Beginner to Expert
Rating: 4.3 (99 reviews)

Master Data Visualization in Python: Learn to Create Compelling Charts and Visual Representations of Your Data.

Created bySheikh Coding Institute

Last updated 6/2025

English

English [Auto],

What you'll learn

Basic Principles of Effective Visualization
Creating Simple Plots (line, bar, scatter)
Customizing Plots (colors, markers, labels)
Distplot and Histogram
Line Charts and Scatter Plots
Bar Charts and Histograms
Dashboards and Interactive Visualizations
Geospatial Data Visualization
Customizing Visualizations with Templates
Creating Interactive Dashboards
Analyzing and Visualizing a Real World Dataset
Building Interactive Dashboards to Explore Data
Deploying Visualizations to Web Platforms

Course content

6 sections • 23 lectures • 3h 1m total length

Basic Principles of Effective Visualization4:49
Master the basic principles of effective visualization by prioritizing clarity, accuracy, and consistency to transform static data into reproducible visuals that support evidence-based, collaborative decision making.
Setting up the Python Environment8:44
Install Python and add it to your path. Create a virtual environment and install numpy and pandas to work with a data frame.

Creating Simple Plots (line, bar, scatter)10:37
Create simple plots in Python with matplotlib, including line, bar, and scatter plots, using titles, labels, and colors to visualize trends, categories, and relationships.
Customizing Plots (colors, markers, labels)8:21
Learn to customize matplotlib plots by adjusting colors, markers, and labels. Configure titles, x and y labels, and font sizes to create informative, visually appealing plots.
Subplots and Figure Size6:29
create subplots and adjust figure size in python using matplotlib, building a 1x2 layout to compare two data series with colors and titles for clear visibility.
Saving Plots6:42
Learn to save plots with matplotlib by exporting figures to png, adjusting dpi for high resolution, and preparing images for reports or presentations.

Introduction to Seaborn8:26
Explore seaborn, a Python visualization library built on matplotlib, to create scatter plots, bar plots, and histograms with the tips dataset.
Distplot and Histogram8:00
Visualize data distribution using distplot and histogram, showing a basic histogram with 30 bins and a kde curve for density estimation with Seaborn.
Box plot and Violin Plot7:47
Visualize data distribution with box plots and violin plots in seaborn, using the tips dataset to compare total bill across days and by gender, with KDE curves.
Scatter Plot and Joint Plot8:20
Explore relationships between variables with scatter plots and joint plots, visualize density with KDE, and practice creating examples using seaborn to reveal patterns in data.
Pair Plot8:19
Explore Seaborn pair plots to visualize relationships among numerical features, with histograms on the diagonals, and see examples using the iris data set, including grouping by species and regression lines.
Heatmap10:11
explore heat maps in seaborn, creating basic and annotated heat maps, and visualize correlation matrices to reveal relationships in tabular data with color scales and custom color maps.

Introduction to Plotly6:57
Learn to build interactive Python visualizations with Plotly, exploring line charts and interactive bar charts, featuring hover details, panning, and export options.
Line Charts and Scatter Plots7:00
Master line charts and scatter plots in Python using Matplotlib to visualize trends over time and relationships between variables, enhanced by markers, grid lines, and color customization.
Bar Charts and Histograms7:57
Explore bar charts for categorical data and histograms for numerical data in Python, using matplotlib to visualize fruit quantities and basic histogram distributions.
Pie Charts and Donut Charts9:28
Learn to create pie and donut charts in Python using Matplotlib to visualize category proportions. Customize colors, labels, and percentages for clear data insight.
3D Plots6:50
Learn to create a 3D scatter plot in Python with Matplotlib, generating random data via NumPy, and visualize relationships among x, y, and z in 3D space.
Dashboards and Interactive Visualizations10:51
Learn to build interactive dashboards in Python using Dash and Plotly, creating a web-based bar chart and an interactive geographic map to visualize categories, values, and population by continent.

Geospatial Data Visualization7:15
Visualize geospatial data in Python using geopandas and pandas, load GeoJSON data, and create interactive maps with folium featuring markers and popups in Jupyter notebooks.
Time Series Visualization7:00
Explore time series visualization in Python with pandas, matplotlib, and Plotly by creating a daily temperature line plot with markers, dates on the x-axis, and Celsius labels.
Statistical Charts (e.g., QQ plots, probability plots)8:06
Explore statistical charts in Python, including double QQ plots and probability plots, to assess data distribution and normality using matplotlib and SciPy with histogram overlays.

Analyzing and Visualizing a Real-World Dataset8:08
Analyze and visualize a real-world dataset with python by loading data with pandas, cleaning missing values, and creating a pair plot with density curves on the diagonal.
Creating a Data Story Using Visualizations5:35
Learn to create a data story in Python by loading a tips dataset, exploring fields like total bill and tips, and visualizing patterns with bar charts.

Requirements

No prior data visualization experience is required
Basic understanding of Python programming (variables, data types, loops, functions).

Description

Are you ready to transform raw data into compelling insights? Do you want to create stunning, informative visualizations that tell powerful stories?

This comprehensive course, "Python Data Visualization Mastery: From Beginner to Expert," will guide you on an exciting journey from the fundamental concepts of data visualization in Python to advanced, professional-grade techniques. Whether you're a complete beginner eager to dive into the world of data science or an experienced developer looking to elevate your visualization skills, this course is meticulously designed to equip you with the knowledge and practical experience you need to succeed.

Why is Data Visualization a Must Have Skill?

In today's data driven world, the ability to effectively communicate insights through visualizations is more crucial than ever. Businesses, researchers, and individuals alike rely on clear, concise, and impactful visual representations to understand complex datasets, identify trends, make informed decisions, and present their findings persuasively. Python, with its rich ecosystem of libraries, stands out as the premier tool for this purpose.

What You'll Learn & Master:

This course takes a hands on, project based approach, ensuring you not only understand the theory but also gain practical experience building a wide array of visualizations. You will master:

Fundamentals of Data Visualization: Understand the principles of effective visualization, choosing the right chart for your data, and avoiding common pitfalls.
Matplotlib: Your foundational library for creating static, interactive, and animated visualizations. Learn to customize every aspect of your plots.
Seaborn: Leverage this high-level interface for drawing attractive and informative statistical graphics. Explore advanced plotting functions for complex datasets.
Plotly & Cufflinks: Build interactive, web-ready visualizations that can be embedded in dashboards and web applications. Discover how to create stunning 3D plots.
Folium: Visualize geographical data and create interactive maps to showcase location-based insights.
Pandas Integration: Seamlessly integrate your data analysis workflows with visualization tools for efficient plotting.
Advanced Customization & Storytelling: Learn techniques to refine your visualizations, add annotations, highlight key insights, and tell a compelling story with your data.
Best Practices for Effective Communication: Understand how to choose appropriate color palettes, use labels effectively, and design visualizations for maximum impact.

By the end of this course, you will be able to:

Confidently select the most appropriate visualization type for any dataset.
Create a wide variety of static, interactive, and geographical plots using Matplotlib, Seaborn, Plotly, and Folium.
Customize your visualizations to meet specific design requirements.
Communicate complex data insights clearly and effectively through compelling visuals.
Build a strong portfolio of data visualization projects.

Enroll now and unlock the power of data visualization with Python! Turn your data into a masterpiece!

Who this course is for:

Anyone looking to gain insights from data
Aspiring Data Scientists & Analysts
Researchers & Students: Effectively present your findings and make your data more accessible.

Python Data Visualization Mastery: From Beginner to Expert

What you'll learn

Explore related topics

Course content

Introduction to Data Visualization2 lectures • 14min

Matplotlib Basics4 lectures • 32min

Seaborn for Statistical Data Visualization6 lectures • 51min

Plotly for Interactive Visualizations6 lectures • 49min

Advanced Data Visualization Techniques3 lectures • 22min

Real-World Data Visualization Projects2 lectures • 14min

Requirements

Description

Who this course is for: