Data Visualization with Python

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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 bootcamp

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.

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.

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.

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.

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

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.

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

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

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