A simple guide to Data Visualization in Python | Choose the right Plots | Determine the Scope

Hands-on Data Visualization Part 1 - https://youtu.be/okyEXF_5KBQ
Hands-on Data Visualization Part 2 - https://youtu.be/zEKUjP0QkhY

In this comprehensive guide to data visualization, we tackle two critical questions. First, we learn choosing the right visualization for your data. Whether you're dealing with numerical or categorical data, making the right choice can significantly enhance the understanding of your dataset.

Next, we delve into the equally important question of how many plots or charts should be created. Striking the perfect balance ensures that your insights are concise, yet comprehensive.

Moving forward, we introduce the fundamental concepts of univariate and bivariate analysis. Under univariate analysis, we cover the histogram, density plot, and boxplot for numerical data. For categorical data, we consider count plots and pie charts to give you a holistic view.

In the realm of bivariate analysis, we explore three pivotal scenarios. When both variables are numerical, the scatter plot emerges as an invaluable tool. For cases where both variables are categorical, we introduce the stacked bar chart, a visual powerhouse. Lastly, when one variable is numerical and the other is categorical, we unveil the secrets of the conditional box plot.

To round off, we guide you through the process of deciding the scope of your visualization in any project. Whether it's a detailed exploration or a concise overview, we provide actionable insights to ensure your visualizations are both impactful and efficient.

Unlock the full potential of your data with this in-depth exploration of data visualization techniques. Create compelling, insightful visualizations that tell your data's unique story. Happy Learning!