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WeCloudData > Programs & Courses > Online > Data Visualization with Python

Data Visualization with Python

Q: How can I get help if I have questions?

You can get support in multiple ways:TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

Online | Self-paced | Start Anytime

Fundamental

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About the Course

This course introduces participants to the fundamental concepts and techniques of data visualization using Python. It covers various types of visualizations to effectively communicate data insights, including line plots, scatter plots, bar charts, geospatial visualizations, and more. By the end of the course, participants will gain hands-on experience with Python’s visualization libraries and be able to create compelling and informative data visualizations.

Curriculum

Module 1: Data Visualization Introduction
Overview:

This module introduces the fundamentals of data visualization and the role it plays in data analysis. It will cover the various types of visualizations and an overview of Python libraries like Matplotlib and Seaborn.

Topics to Cover:

Importance of data visualization in decision-making

Common types of visualizations (e.g., line plots, bar charts)

Overview of popular visualization libraries: Matplotlib, Seaborn, Plotly, Folium

Basic plotting syntax in Python
Module 2: Line Plot
Overview:

This module focuses on line plots, one of the most commonly used methods for visualizing trends and time series data. Participants will learn how to create, customize, and annotate line plots.

Topics to Cover:

Introduction to Matplotlib

Simple line plots

Customizing labels, and axes
Module 3: Scatter Plot
Overview:

This module teaches how to use scatter plots to visualize relationships between two variables. Participants will explore how to customize scatter plots to reveal patterns and trends more effectively.

Topics to Cover:

Creating scatter plots with Matplotlib

Customizing scatter points (size and color)

Identifying correlations between data points
Module 4: Regression Plot
Overview:

This module focuses on plotting and visualizing regression models to analyze trends. Participants will explore how to represent linear relationships between variables and customize regression plots for better clarity and interpretation.

Topics to Cover:

Introduction to regression plots with Matplotlib and Seaborn

Visualizing linear relationships between variables

Customizing regression plots (colors and markers)
Module 5: Multiple Plots
Overview:

This module covers how to create and organize multiple plots in one figure using subplots. Participants will learn how to compare datasets visually by arranging different plots side-by-side.

Topics to Cover:

Using plt.subplots() to create multiple figures

Customizing subplot layouts (grid, row, and column formats)

Sharing axes and adjusting spacing between subplots

Combining different plot types in a single figure
Module 6: Box Plot
Overview:

This module teaches the use of box plots to visualize data distribution and detect outliers. Participants will learn to interpret the five-number summary of datasets through box plot representation.

Topics to Cover:

Creating box plots with Matplotlib

Understanding the five-number summary (min, Q1, median, Q3, max)
Module 7: Area Plot
Overview:

This module covers how to create area plots to represent cumulative data over time. Participants will learn to customize area plots and visualize stacked data layers effectively.

Topics to Cover:

Creating simple and stacked area plots

Customizing area plot styles (rotation, legends and titles)

Analyzing trends and comparisons in cumulative data
Module 8: Histogram
Overview:

This module introduces histograms as a way to analyze the distribution of numerical data. Participants will learn how to create and customize histograms for effective distribution analysis.

Topics to Cover:

Creating histograms with Matplotlib

Customizing bin size and label
Module 9: Bar Plot
Overview:

This module focuses on bar plots to represent categorical data. Participants will learn how to create vertical bar plots and customize them for clarity.

Topics to Cover:

Creating bar plots with Matplotlib

Customizing bar width, color, and labels

Plotting grouped and stacked bar charts

Adding annotations to bar plots
Module 10: Pie Chart
Overview:

This module covers the creation of pie charts to visualize proportions of a whole. Participants will learn how to customize pie charts to enhance the representation of data.

Topics to Cover:

Creating basic pie charts with Matplotlib

Customizing pie chart slices (explode, colors)

Adding labels and percentages to pie charts

Limitations and best practices for using pie charts
Module 11: Word Cloud
Overview:

This module teaches participants how to create word clouds for visualizing text data. Participants will learn to customize word clouds based on text frequency and relevance.

Topics to Cover:

Introduction to WordCloud library in Python

Creating simple and customized word clouds from text data

Customizing word cloud size, color, and shape

Filtering out stop words and irrelevant text

Applications of word clouds in text analysis
Module 12: Folium Geospatial Maps
Overview:

This module explores geospatial data visualization using the Folium library. Participants will learn to create interactive maps and represent geographic data visually.

Topics to Cover:

Introduction to Folium for geospatial data

Creating interactive maps with Folium

Adding markers and popups to maps

Visualizing geospatial datasets
Module 13: Additional Exercises & Mini-Projects
Overview:

This module provides hands-on exercises and mini-projects to apply the skills learned throughout the course. Participants will work on projects to create comprehensive visualizations and present their findings.

Topics to Cover:

Practice exercises for different plot types

Mini-projects for data visualization storytelling

Creating comprehensive dashboards using multiple visualization types

Presenting visualizations and insights effectively

Learning Outcomes

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

Understand the principles and importance of data visualization.
Utilize Python libraries to create a variety of visualizations effectively.
Apply visualization techniques to analyze and present data insights.
Develop practical skills through hands-on projects and exercises.

Tools

Jupyter Notebook

Python Libraries: Pandas, Matplotlib, Seaborn, Folium, WordCloud

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FAQ

Frequently asked questions about the course

What is the structure of the course?

The course is structured into weekly modules, each containing video lectures, reading materials, assignments, and quizzes. You can complete the modules at your own pace, but we recommend following the weekly schedule to stay on track.

How can I get help if I have questions?

You can get support in multiple ways:

TA Support on Slack: Our teaching assistants are available on Slack to answer your questions and provide guidance.
Peer Community on Discord: Join our Discord community to discuss course topics, share ideas, and collaborate with fellow students.

What are the hours of availability for TAs on Slack?

TAs are available on Slack from 9 AM to 6 PM (ET) Monday to Friday. Outside these hours, you can still post your questions, and TAs will respond as soon as they are back online.

How do I join the Discord community?

After enrolling in the course, you will receive an invitation link to join the Discord community. Follow the link to create an account or log in to your existing account.

What kind of support can I expect from the Discord community?

The Discord community offers peer-to-peer support, where you can discuss course topics, share resources, collaborate on projects, and network with fellow learners

What does the optional mentoring service include?

The optional mentoring service includes one-on-one sessions with an experienced mentor who can provide personalized guidance, feedback on your progress, and help you set and achieve your learning goals.

How do I sign up for the mentoring service?

Please talk to our Program Advisors to sign up for Mentorship services for an additional cost

Can I access the course materials after completing the course?

Yes, you will have lifetime access to the course materials, including any updates made to the content in the future.

What payment methods are accepted for enrolling in the course?

We accept all major credit cards, PayPal, and bank transfers. You can choose your preferred payment method at checkout

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Data Visualization with Python

About the Course

Curriculum

Module 1: Data Visualization Introduction

Module 2: Line Plot

Module 3: Scatter Plot

Module 4: Regression Plot

Module 5: Multiple Plots

Module 6: Box Plot

Module 7: Area Plot

Module 8: Histogram

Module 9: Bar Plot

Module 10: Pie Chart

Module 11: Word Cloud

Module 12: Folium Geospatial Maps

Module 13: Additional Exercises & Mini-Projects

Learning Outcomes

Tools

Early Access Available!

Join the Waitlist

Upgrade your skills with our short courses

Ranked #1 Data Training Program

student success

What our graduates are saying

Recommended if you're interested in Data Visualization with Python

MLOps Engineer Track

Big Data Engineer Track

Cloud Engineer Track

Large Language Model (LLM) Engineer Track

Data Streaming

Data Migration

Data Lake Architecture

AI Autiomation and RPA

Career Track to Advance Your Career

Unlock Your Potential with Expert Guidance

Empower Your Workforce

FAQ

Ready to kick start your career

Speak to our advisors

Sign up for newsletter

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Company

Let’s Connect!