Welcome to the second course in the Introduction to Applied Data Science with Python specialization. In the first course we looked, primarily, at how to manipulate data, including loading data from structured delimited files, such as CSVs, cleaning the data with Python pandas, and NumPy libraries, and transforming data using functions, such as apply, aggregate, map, and pivot. If you don't feel you have a solid understanding of the pandas toolkit, then I would invite you to go back and review that course content. The main topic of this course is understanding how to communicate data more clearly to the various stakeholders who are interested in the data. And there are many different stakeholders to whom you might be interested in using visuals to reach. For instance, you want to share unbiased, representations of data with peer data scientists who are able to make their own inferences and interpretations. But you'll also want to be able to share stories about the data with managers and executives who might be familiar with domain but are looking for specific actionable insights. Finally, you'll also want to be able to communicate like a journalist to a broad population who knows very little about your particular data and needs to understand not only an overview of the phenomenon you are describing but also the limitations of the approaches you have used. This course is about specific techniques which are part of a broad subject of information visualization. Referred to an infovis for short, this is a field that has rapidly developed over the past 20 years, as computing power has made it possible to build compelling visual and interactive graphics quickly. And while the use of visualizations to impart knowledge of data has been around for centuries, the last 20 years have seen the development of important theoretical basis for best practices in displaying information to various audiences. And in this course, we're going to focus on those. In this first week of the course, we're going to touch on some of those theories and best practices, and talk about fundamental thinkers in the field. In particular, Alberto Cairo and Edward Tufte. We'll go through some examples of excellent and not so excellent visualizations. And develop a language for speaking about how we might make meaningful visualizations ourselves. This portion of the course will be largely lecture based with several readings and peer reviewed assignment at the end of each week. In the second and third week of the course, we'll dive into the de facto Python library for creating charts and graphs, matplotlib. Now matplotlib isn't the only way to create visual artifacts in Python, but it's the basis for some emerging new web tools such as Seaborn and Bokeh. And acts as a solid foundation for our discussions. The majority of these weeks will be spent actually working on assignments with a few lectures to share an overview of how the toolkit itself works. And you'll be expected to take messy raw data, process it using pandas or other suitable techniques and render it into a meaningful, explanatory image. And again, if you're not comfortable yet using pandas, I would encourage you to go back to the first course in this specialization and review those videos. The assignments for weeks two and three will be largely peer graded. Finally, the course will culminate with a small project, similar to the first course. In this project, you will be given a new visualization technique and you will be asked to extend the matplotlib code base to enhance its functionality. This is a critical step in your journey as a data scientist. To go beyond using tools off the shelf and instead be able to modify them to support new ways of understanding data and problems. This course is very similar to the first, except the evaluation is largely peer review. The lectures will only guide you so much in completing the assignments. You'll need to know how to ask questions in the discussion forums of your peers. And seek out new information through web searches and Stack Overflow. And again, Stack Overflow is tagged specifically for questions about matplotlib. So I'd encourage you to use that resource as well. Finally, for this course, I expect that you are familiar with all of the content from Course 1. I'm looking forward to this next course in specialization. Let's get started.
