Data Visualization Fundamentals in Python

Data Visualization Fundamentals in Python

This course is part of Data Visualization: Fundamentals to Interactive Storytelling Specialization

Instructor: P.J. Grosse

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

Develop interactive visualizations using Python libraries.
Implement data visualization techniques and plots using Python libraries, such as Matplotlib and Seaborn to tell a compelling story
Customize visualization features and styles with themes, colors, and chart elements for clear communication.
Apply human perception principles to enhance the effectiveness and interpretability of your visualizations.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Data Visualization: Fundamentals to Interactive Storytelling Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

The "Data Visualization Fundamentals in Python" course empowers you to transform data into compelling visual narratives. Dive into the principles and best practices of data visualization, blending the art and science to tell impactful data stories using Python.

Through this course, you will master creating basic and advanced data visualizations and learn to effectively communicate complex information visually. You will explore Python libraries such as Matplotlib and Seaborn and understand how to customize visual elements to create clear, insightful, and visually appealing data representations. You will develop skills to apply fundamental principles, incorporate human perception aspects, customize your plots, and create interactive visualizations ensuring an accurate interpretation of data. This course will equip you with techniques for transforming complex datasets into coherent visuals, optimizing your ability to convey data-driven insights effectively.

This module introduces the goals of the course, and provides an overview of what data visualization is, why does it matter, and reviews historical examples of data visualizations.

What's included

5 videos8 readings2 assignments

5 videos Total 30 minutes

Welcome to the Art of Data Visualization 3 minutes
Data Visualization: what stories can we tell? 1 minute
Data Visualization Hall of Fame 13 minutes
Why is Data Visualization Important? 6 minutes
Art vs. Science of Data Visualization 7 minutes

8 readings Total 83 minutes

Syllabus 10 minutes
Navigation Help 10 minutes
Technical Support 10 minutes
Data Visualization Timeline 5 minutes
Visualizing Ourselves 18 minutes
Bad Data Visualization 10 minutes
Module 1 Wrap Up 10 minutes
Module 1 Sources 10 minutes

2 assignments Total 45 minutes

Module 1 Knowledge Check 30 minutes
Let's Practice: Introduction to The Art of Data Visualization 15 minutes

This module teaches the principles of visualization, what makes visualizations effective, and how different data types can be visualized.

What's included

5 videos5 readings2 assignments

5 videos Total 25 minutes

Principles of Perception 2 minutes
Principles of Visualization 6 minutes
Effective Data Visualization 6 minutes
What to Visualize 7 minutes
How to Visualize 5 minutes

5 readings Total 115 minutes

Module 1 Recap 10 minutes
Data Visualization Resources 45 minutes
Expressiveness in Visualization 20 minutes
Storytelling with Data Chart Guide 30 minutes
Module 2 Sources 10 minutes

2 assignments Total 45 minutes

Module 2 Knowledge Check 30 minutes
Let's Practice: Principles of Data Visualization 15 minutes

This module introduces basic Python programming within Jupyter notebook environment.

What's included

34 videos2 readings2 assignments

34 videos Total 299 minutes

The Python Programming Environment 12 minutes
Basic Scripts, Arithmetic, and Comments 8 minutes
Getting Started with Jupyter 20 minutes
Using Jupyter Notebooks 11 minutes
Managing Execution in Jupyter, Introducing Data Types 9 minutes
Strings, Booleans, Indexing, and Slicing 15 minutes
Lists, Indexing, and Slicing 6 minutes
For Loops 12 minutes
Heterogenous Lists, Exporting Jupyter Notebooks 7 minutes
1D NumPy Arrays 10 minutes
NumPy Array Attributes and Creation 9 minutes
Arrays vs. Lists 13 minutes
Manipulating 1D Arrays: Slicing and Indexing 8 minutes
Conditional Slicing 9 minutes
Combining Arrays 8 minutes
Sorting and Summarizing Arrays 10 minutes
Summarizing Arrays, Continued 9 minutes
What is a 2D Array? 7 minutes
2D Array Attributes and Creation 6 minutes
Creating 2D Arrays from 1D Arrays 7 minutes
Manipulating 2D Arrays: Slicing and Indexing 7 minutes
Conditional Subsetting 2D Arrays 10 minutes
Slicing, Subsetting, and Side Effects 9 minutes
Summarizing 2D Arrays 5 minutes
Introduction to Pandas Series: Basics and Practical Application 8 minutes
Pandas Series Indexing and Slicing: A Deep Dive 7 minutes
Pandas Series: Defining Indices and Handling Non-Unique Values 7 minutes
Series Concatenation in Pandas 8 minutes
Introduction to Pandas DataFrames: Converting NumPy Arrays and DataFrame Operations 7 minutes
Distinguishing Pandas DataFrames from NumPy 2D Arrays 6 minutes
Converting Dictionary to DataFrame in Pandas 8 minutes
DataFrame Attributes and Methods in Pandas: Understanding dtypes, info(), and describe() 7 minutes
Descriptive Statistics and Sorting in Pandas DataFrames 7 minutes
DataFrame Sorting and Management in Pandas 7 minutes

2 readings Total 100 minutes

Basics of Python - A Review 10 minutes
Module 1 Reading 90 minutes

2 assignments Total 45 minutes

Module 3 Knowledge Check 30 minutes
Let's Practice: Review of Python Basics 15 minutes

This module teaches creating and customizing data charts using Python libraries of Matplotlib.

What's included

4 videos9 readings2 assignments1 discussion prompt1 ungraded lab

4 videos Total 27 minutes

Python for Data Visualization 1 minute
Introduction to Matplotlib 4 minutes
Creating Simple Plots 8 minutes
Advanced Plotting with Matplotlib 13 minutes

9 readings Total 100 minutes

Module 2 Recap 10 minutes
Introduction to Matplotlib 10 minutes
Matplotlib Tutorials 10 minutes
Practice Simple Plots 20 minutes
1 Dataset, 100 Matplotlib Visualizations 10 minutes
Comical Data Visualization in Python Using Matplotlib 10 minutes
Practice Advanced Plots 10 minutes
Matplotlib Practice Lab Solution 10 minutes
Module 4 Sources 10 minutes

2 assignments Total 45 minutes

Module 4 Knowledge Check 30 minutes
Week 4 #8-10 Pandas Dataframes 2 15 minutes

1 discussion prompt Total 20 minutes

Matplotlib Practice Lab Discussion 20 minutes

1 ungraded lab Total 60 minutes

Matplotlib Practice Lab 60 minutes

This module teaches advanced charting using Python libraries such as Seaborn and provides a contrast between Seaborn vs Matplotlib libraries for data visualization.

What's included

3 videos9 readings2 assignments1 discussion prompt3 ungraded labs

3 videos Total 23 minutes

Introduction to Seaborn Library 8 minutes
Seaborn Plotting Functions 6 minutes
Advanced Plotting with Seaborn 9 minutes

9 readings Total 90 minutes

Module 4 Recap 10 minutes
Matplotlib vs. seaborn vs. Plotly vs. MATLAB vs. ggplot2 vs. pandas 10 minutes
Generate a Scatterplot with Seaborn Lab Solution 10 minutes
Seaborn Tutorials 10 minutes
Generate a Histogram with Seaborn Lab Solution 10 minutes
Seaborn Tutorial [Optional] 10 minutes
Module 5 Wrap up 10 minutes
Seaborn Practice Lab Solution 10 minutes
Module 5 Sources 10 minutes

2 assignments Total 45 minutes

Module 5 Knowledge Check 30 minutes
Let's Practice: Making Figures with Python: Advanced Plotting 15 minutes

1 discussion prompt Total 30 minutes

Seaborn Practice Lab Discussion 30 minutes

3 ungraded labs Total 130 minutes

Generate a Scatterplot with Seaborn 10 minutes
Generate a Histogram with Seaborn 60 minutes
Seaborn Practice Lab 60 minutes

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

This course is part of the following degree program(s) offered by University of Pittsburgh. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

P.J. Grosse

University of Pittsburgh

4 Courses 1,447 learners

Offered by

University of Pittsburgh

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