Course Introduction

[MUSIC] 0:00 Hello, I'm Ken. 0:04 In this course, I'm excited to introduce you to the Python library, Bokeh. 0:07 Bokeh is a Python interactive visualization library 0:12 that targets modern web browsers for presentation. 0:16 Without using a dedicated Python library power visualizations, 0:19 we might rely on a non-Python library, such as D3.js for our work. 0:24 Not that there is anything wrong with D3.js, it is a great and 0:29 powerful JavaScript library. 0:32 However, since we are working in Python already for data processing, 0:34 it seems to make sense to stick with our one language if we're able to. 0:39 Behind the scenes, Bokeh generates the JavaScript, HTML, and CSS for us. 0:43 Therefore, instead of having to manage multiple languages and libraries, 0:49 Bokeh consolidates it into one, relatively easy step. 0:54 Bokeh allows for interactive data visualization, 1:00 so that users can explore data themselves. 1:03 This is one reason why I like it better than libraries like Matplotlib, 1:06 which has been used for a long time for more basic plotting and charts. 1:10 Data visualization is an important part of a variety of developer roles today. 1:15 Whether you're doing pie charts in Excel, or 1:20 graphing time series data from a scientific experiment, 1:22 how you present your data can make a large impact on how people understand the data. 1:26 What's important about that, you might ask. 1:31 Well, think about what happens when you add a bar chart to a spreadsheet 1:34 to quickly determine and visualize your data results. 1:37 Spreadsheets are great at displaying rows of data and 1:41 their associated column topics. 1:44 But they are not super great themselves for seeing if there are patterns in data. 1:46 This is where data visualization can help with a variety of charts, bar, line, 1:50 pie, for example, or plots, like box, scatter, or candlestick. 1:55 Say for example, that we have a spreadsheet table of data that shows miles 2:00 per gallon, or MPG of a car, based on the number of cylinders in the engine. 2:03 Looking at the table itself can be a daunting task to 2:09 come up with any exacting information. 2:12 If however, we apply some visualization to our data, we could use a bar graph 2:14 to showcase general buckets to see a comparison of MPG to cylinders. 2:18 Or a scatter plot, to show each specific entry in our data, and 2:23 where it falls in comparison to others. 2:27 Or we could visualize the data in a box chart format to 2:29 see a more clear comparison of the average, mean, minimum, and 2:33 maximum MPG for the various engine sizes. 2:37 Bokeh's strength is that it allows for 2:40 a wide variety of interactive plots that are quickly and easily generated. 2:42 Further, adding interactivity to your visualizations is often a simple process, 2:47 and allows for users to examine and explore data further and deeper 2:52 without a lot of extra time being spent on the development side of the project. 2:56 If we take a quick look at the Bokeh website, we see that there is some good 3:01 information in their user guide, especially on how to get started, 3:04 as well as some interesting visualization projects available in the gallery section. 3:09 I put this link in the teachers notes. 3:14 So be sure to check it out to learn more about what Bokeh offers 3:16 that we won't be covering in this course. 3:20 We'll be relying on some Python libraries. 3:22 Like NumPy and pandas to work with our data as well, 3:25 showing how to use them with Bokeh's column data source. 3:29 But we won’t be going into pandas data frames or numPy arrays. 3:32 I'll assume that you have a basic understanding of what they are and 3:36 how to generally work with them. 3:39 Again, I put links in the teacher's note for a refresher if needed. 3:41 There are a lot of public data sets available for exploration. 3:45 We're going to be exploring and visualizing world population numbers 3:48 from worldwide data.info and their world country population data sets. 3:52 The data set we'll be using also includes three-letter country codes added to it, 3:57 which will be convenient for use later on. 4:01 Now that we have some of the basic project requirements covered, 4:05 let's take a quick break before we start looking at some code. 4:07