You will learn hands-on by completing numerous labs and a final project to practice and apply the many aspects and techniques of Data Visualization using Jupyter Notebooks and a Cloud-based IDE. You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash.

Visualization helps us understand and retain insights from the data we present to the stakeholders. Throughout this course, I learned how to effectively visualize each piece of data for a better understanding using the appropriate graphical …

Apr 28, 2025 · Data visualization plays an essential role in the representation of both small and large-scale data. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.

Master Python data visualization using Matplotlib, Seaborn, and Plotly to create stunning interactive charts and dashboards. Transform complex datasets into compelling visual stories through hands-on exploratory analysis.

Jul 23, 2023 · This article will guide you to accomplish the final assignment of Data Visualization with Python, a course created by IBM and offered by Coursera. Nevertheless, this tutorial is for anyone— enrolled in the course or not — who wants to learn how to code an interactive dashboard in Python using Plotly’s Dash library.

Learn various tools of data visualisation and tell a compelling story with Coursera’s Data Visualization with Python ...Read more. The Data Visualisation with Python course, offered by IBM via Coursera, is designed to teach learners how to …

Description: This course delves into the world of data analysis with Python. You'll learn how to use libraries like pandas and Matplotlib to manipulate, analyze, and visualize data, extracting valuable insights and communicating findings effectively.

The techniques and tools covered in Data Visualization with Python are most similar to the requirements found in Data Analyst job advertisements. "A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the anal.

Key Points: Use Pandas to produce a table to visualize the data. Produce a data plot using MatPlotLib Pyplot. Use Seaborn to create a scatterplot graph. Create a Jointplot to show distribution. Create a heatmap to show correlations and distributions.

In this module, you will learn about data visualization and some key best practices to follow when creating plots and visuals. You will discover the history and the architecture of Matplotlib. Furthermore, you will learn about basic plotting with Matplotlib and explore the dataset on Canadian immigration, which you will use during the course.