Python for Data Visualization and Analysis

This course is part of Applied Data Analytics Specialization

Instructor: Edureka

What you'll learn

Create impactful visualizations using Matplotlib and Seaborn to represent complex datasets effectively.
Build dynamic, interactive charts and dashboards with Plotly and IPyWidgets for enhanced data exploration.
Build dynamic, interactive charts and dashboards with Plotly and IPyWidgets for enhanced data exploration.
Deploy interactive data visualization applications seamlessly with Streamlit to share analysis results.

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Assessments

13 assignments

Taught in English

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This course is part of the Applied Data Analytics Specialization

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There are 4 modules in this course

This course offers a hands-on introduction to data visualization and exploratory data analysis (EDA) using Python's most popular libraries. You'll dive deep into creating stunning visuals with Matplotlib and Seaborn, building interactive charts and dashboards with Plotly, and conducting EDA on complex datasets through advanced graphical methods. Explore how to present data effectively and gain actionable insights from visual representations.

By the end of this course, you’ll be able to: - Understand the importance of various visualization techniques. - Select appropriate chart types for visualizing diverse datasets. - Create professional-quality visuals with Matplotlib, Seaborn, and Plotly. - Develop interactive dashboards and visuals with Plotly and IPyWidgets. - Perform EDA on complex datasets and deploy the results using Streamlit. This course is ideal for learners with foundational knowledge of Python programming and a basic understanding of data manipulation. Familiarity with libraries such as Pandas or NumPy is recommended. Whether you're a data analyst, aspiring data scientist, or Python programmer looking to sharpen your data visualization skills, this course equips you with the tools to transform raw data into meaningful stories. Elevate your data analysis journey—enroll in Data Visualization and Exploratory Data Analysis with Python today!

In this module, learners will explore how to create various types of visualizations using Matplotlib. They will learn to apply these visuals to complex datasets, uncovering hidden insights that facilitate informed decision-making.

What's included

17 videos5 readings4 assignments1 discussion prompt

17 videosTotal 51 minutes

Course Introduction2 minutesPreview module
Environment Set-Up2 minutes
Importance of Data Visualization2 minutes
Line Plot2 minutes
Bar Chart1 minute
Horizontal Bar Chart1 minute
Stacked Bar Chart1 minute
Histogram1 minute
Demonstration: Plotting Line and Bar Graph6 minutes
Demonstration: Plotting Histogram4 minutes
Scatter Plot3 minutes
Pie Chart3 minutes
Box Plot4 minutes
Customizing Charts2 minutes
Demonstration: Pie Chart2 minutes
Demonstration: Scatter Plot and Box Plot4 minutes
Summary of Visualization with Matplotlib3 minutes

5 readingsTotal 95 minutes

Welcome to Python for Data Visualization and Analysis10 minutes
From Numbers to Narratives20 minutes
Choosing the Right Chart: Bar Charts, Line Charts and Histogram25 minutes
Choosing the Right Chart Type20 minutes
Choosing the Right Chart: Scatter, Pie and Box20 minutes

4 assignmentsTotal 29 minutes

Knowledge Check : Visualizing Data with Matplotlib20 minutes
Practice Quiz : Setting Up Matplotlib3 minutes
Practice Quiz : Types of Plots and Charts3 minutes
Practice Quiz : Plotting Different Charts3 minutes

1 discussion promptTotal 10 minutes

Introduce Yourself10 minutes

In this module, learners will delve into data visualization with Seaborn, mastering the creation of diverse plots while developing skills to customize and refine visuals for improved presentation and interactivity.

What's included

12 videos2 readings3 assignments1 discussion prompt

12 videosTotal 41 minutes

What is Seaborn?2 minutesPreview module
Installing and Setting Up Seaborn1 minute
Comparing Seaborn with Matplotlib2 minutes
Relational Plot (Rel plot)1 minute
Distribution Plot (Dist Plot)1 minute
Categorical Plot (Cat Plot)2 minutes
Demonstration: Visualizing Charts with Seaborn4 minutes
Demonstration: Visualizing HeatMap2 minutes
Demonstration: Category, Relational and Distribution Plots 5 minutes
Demonstration: Personalizing Charts and Visuals6 minutes
Demonstration: Tailoring Graphs and Visuals6 minutes
Summary for Data Visualization with Seaborn 3 minutes

2 readingsTotal 40 minutes

Seaborn with Matplotlib20 minutes
A Guide to Seaborn20 minutes

3 assignmentsTotal 26 minutes

Knowledge Check : Visualizing Data with Seaborn20 minutes
Practice Quiz : Seaborn Library3 minutes
Practice Quiz : Plot Types in Seaborn3 minutes

1 discussion promptTotal 10 minutes

Which of the following is easier to use, Seaborn or Matplotlib?10 minutes

In this module, learners will explore how to create interactive plots using Plotly, enhance exploratory data analysis (EDA) with IPyWidgets, and build shareable web applications with Streamlit. They will also gain the skills to develop dynamic dashboards and interactive reports for effective data presentation.

What's included

24 videos3 readings5 assignments1 discussion prompt

24 videosTotal 98 minutes

Plotly3 minutesPreview module
Customizing Basic Plot - Background and Markers3 minutes
Customizing Basic Plot - Lines, Titles and Labels 3 minutes
Customizing Basic Plot - Interactive2 minutes
Interactive Plots3 minutes
Demonstration: Plots with Hover Feature3 minutes
Demonstration: Customizing Hover Features and Tooltips4 minutes
Plotly Dash5 minutes
Demonstration: Defining Layout and Structure3 minutes
Demonstration: Building Web Apps 4 minutes
Demonstration: Chaining Callbacks4 minutes
Demonstration: Multiple Inputs and Outputs with Interactions4 minutes
Demonstration: Importing Airbnb Data2 minutes
Demonstration: Web App for Airbnb Data3 minutes
IPyWidgets5 minutes
Displaying Widgets Layouts and Container Widgets3 minutes
Interactive Controls Combining Multiple Widgets for Interactivity5 minutes
Custom Widgets Creating and Registering Custom Widgets4 minutes
Extending Widget Functionality5 minutes
What is Streamlit?2 minutes
Demonstration: Code Details 6 minutes
Demonstration: Executing the App4 minutes
Demonstration: Data Visualization on Streamlit5 minutes
Summary for Interactive Data Visualization3 minutes

3 readingsTotal 40 minutes

Turning Static Plots Interactive20 minutes
Plotly dash: Best Practices10 minutes
Building with Streamlit10 minutes

5 assignmentsTotal 32 minutes

Knowledge Check : Interactive Visuals with Plotly and IPyWidgets20 minutes
Practice Quiz : Plotly Library3 minutes
Practice Quiz : Plotly Dashboard3 minutes
Practice Quiz : Working of IPyWidgets3 minutes
Practice Quiz : Streamlit3 minutes

1 discussion promptTotal 10 minutes

How do IPyWidgets enhance the interactivity of Jupyter Notebook projects?10 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on data visualization concepts, Matploltlib, Seaborn, Plotly and Association rule mining.