Introduction

"How does this data make sense to the business?" It's a critical question you'll need to ask every time you start working with a new, raw dataset. Even after you clean and prepare raw data, you won't be able to derive actionable insights from it just by scanning through thousands of rows and columns. To be able to present the data in a way that it provides value to the business, you may need group similar rows, re-arrange the columns, generate detailed charts, and more. Manipulating and visualizing the data to uncover insights that stakeholders can easily understand and implement is a key skill in a marketing analyst's toolbox. This chapter is all about learning that skill.

In the last chapter, you learned how you can transform raw data with the help of pandas. You saw how to clean the data and handle the missing values after which the data can be structured into a tabular form. The structured data can be further analyzed so that meaningful information can be extracted from it.

In this chapter, you'll discover the functions and libraries that help you explore and visualize your data in greater detail. You will go through techniques to explore and analyze data through solving some problems critical for businesses, such as identifying attributes useful for marketing, analyzing key performance indicators, performing comparative analyses, and generating insights and visualizations. You will use the pandas, Matplotlib, and seaborn libraries in Python to solve these problems.

Let us begin by first understanding how we can identify the attributes that will help us derive insights from our data.