In previous chapters, we’ve talked about the most fundamental thing in data analysis and data science—the data itself—along with statistics, which is the foundation for everything else in data science. You obviously can’t do anything in data science without data. But you also can’t just take some raw data and start pulling out fascinating insights or predictions from it. There are a lot of steps to carry out before the data can yield valuable information. In fact, there are many ways that data can be explored, and the field of data analysis is devoted to understanding and working with data. Data analysis primarily involves slicing and dicing data in well-informed ways to extract meaning from it with analytical tools. These tools include programming languages, spreadsheets, and techniques from the statistics we covered in the previous three chapters, especially charts and other visualizations. Often data analysts stick to descriptive statistics, but some will delve into the more advanced statistics. Data analysts have been around for a long time, looking at what has happened before, explaining what it means, and sometimes using that information to help us understand the world and predict the future.