Data Visualization with Python

Bring your data to life with powerful visuals. Numbers alone don’t tell a story—visuals do. With Python, you can turn raw data into clear, impactful charts that make insights visible and easy to understand. Data Visualization with Python introduces you to the libraries and techniques that transform your analysis into compelling visuals. We begin with Matplotlib, the core visualization library, to build essential charts such as line plots, bar charts, scatter plots, and histograms. You’ll learn how to customize colors, labels, and layouts so your charts communicate clearly. From there, we move to Seaborn, which makes it easier to create beautiful, statistically rich graphics with minimal code. You’ll explore heatmaps, pair plots, and advanced categorical plots that help uncover deeper patterns in your data. You’ll also practice integrating visualization into your analysis workflow: using plots to check data quality, validate assumptions, and present results. By working with real datasets, you’ll learn how to choose the right chart for the right story and how to avoid common pitfalls like clutter or misleading axes. By the end of this course, you will be able to: Create a variety of charts using Matplotlib and Seaborn. Customize visuals for clarity and impact. Use visualization to explore, validate, and communicate data. Build simple dashboards and reports combining multiple plots. Tell a story with data that resonates with any audience. As with every Hands-on Mentor course, the focus is practical. You won’t just see how charts are made—you’ll create them, adjust them, and use them to explain real insights. Data Visualization with Python equips you with one of the most important skills in data analysis: the ability to communicate results in a way that drives decisions.