Hello everyone and welcome to data visualization with Python. I'm Alex Aklson, a data scientist at IBM, and I'm your instructor for this course. Throughout this course we're gonna learn how to create meaningful, effective, and aesthetically pleasing data visuals and plots in python using Matplotlib and a couple of other libraries namely Seaborn and Folium. This course will consist of three modules. In module 1, we will briefly discuss data visualization and some of the best practices to keep in mind when creating data visuals. We will then learn about Matplotlib: its history, architecture, and the three layers that form its architecture. We will also learn about the data set that we will use throughout the course in these lectures as well as the hands-on sessions. We will essentially be working with a data set that was curated by the United Nations on immigration from different countries to Canada from 1980 to 2013. Then we will start learning how to use Matplotlib to create plots and visuals, and we will start off with line plots. Now, we will generate the majority of our plots and visualizations in this course using data stored in pandas dataframes. For those of you who don’t know what pandas is, pandas is a python library for data manipulation and analysis. So before we start building visualizations and plots, we will take a brief crash course on pandas and learn how to use it to read data from csv files like the one shown here into what is called a pandas dataframe like the one shown here. Now, if you are interested in learning more about the pandas library, we actually cover it in much more detail in our next course in this specialization which is Data Analysis with Python, so make sure to complete the next course in this specialization. In module 2, we will continue on with a few more basic data visualizations such as area plots, histograms, and bar charts, and learn how to use Matplotlib to create them and even create different versions of these plots. We will also cover a set of specialized visualizations such as pie charts, box plots, scatter plots, and bubble plots, and we will learn how to create them still using Matplotlib. In module 3, we will learn about more advanced visuals such as waffle charts that provide a fine-grained view of the proportions of different categories in a dataset. We will also learn about word clouds that depict word frequency or importance in a body of text. Also, in this module, we will explore another library, seaborn, which is built on top of Matplotlib to simplify the process of creating plots and visuals, and we will get a taste of its effectiveness through the creation of regression plots. Finally, in this module, we will explore another library, folium, which was built primarily to visualize geospatial data. So, we will learn how create maps of different regions of the world, superimpose markers of different shapes on top of maps, and learn how to create choropleth maps. Before I conclude this video, let me stress one thing. Data visualization is best learned through hands-on exercises and sessions. Therefore, don’t worry if you find some of the videos to be short. The labs and the hands-on sessions are very thorough and cover a lot of the concepts that are discussed in the videos in much more detail, so it is very important that you complete the labs and the hands-on sessions, although they are ungraded components of the course. I hope that you remember this and keep it in mind as you progress in this course. After completing this course, you’ll be able to use different visualization libraries in Python namely, Matplotlib, seaborn, and folium to create expressive visual representations of your data for different purposes. So, let’s get right into it.
