Welcome to the final week of activities for this core. The key goal this week is to consolidate the various concepts we've learned so far. This includes data files, persistent, repositories of information stored in structured formats. Also information visualization, graphical summaries of large-scale data and loops, which are means of repeatedly executing the same computation. In terms of data files, we're going to look in particular this week at CSV files, where the values are separated by commas. For information visualization, we're going to look at x, y, scatter plots of data in two dimensions. Then as far as loops are concerned, we're going to revisit our friend, the for loop, the counted loop. In this video, we'll outline the steps required to complete the next hands-on coding activity, which brings all these concepts together. We're going to look at the algorithm that we want you to implement. Remember that an algorithm is a list of steps to follow in order to solve a problem, named after Al Khwarizmi, who is depicted on this 1980s Soviet era postage stamp here. The dataset we're going to be studying is called the Iris dataset. This is a very famous dataset first used by British scientist Ronald Fisher in 1936. In this dataset, there are three different varieties of iris flowers, 50 of each flower, so 150 flowers in total. The dataset records four measurements for each flower. Here's an iris petal. Each iris flower has three petals, and then in-between each pair of petals, each iris flower has a sepal, so there are going to be three sepals as well. These are the two different components in the iris flower, the petals and sepals. As far as the data we're measuring goes, we're going to take the length of the petal and the width of the petal. We'll just do that for one petal in each flower, and then we're going to take the length of the sepal and also the width of the sepal. Again, we'll just do that for one sepal for each flower. That gives us four measurements for each flower, petal length and petal width. Also, sepal length and sepal width. These four measurements are taken for each flower and we measure in centimeters. This is the iris.csv dataset. You can see there are five columns, the names here in the header row. First four columns are the measurements of the petal and sepal length and width, for each flower. The fifth column indicates the variety of Iris. First of all, iris-setosa, then iris-versicolor, and finally, iris-virginica, and here are 50 of each variety of iris making a total of 150 observations, plus the one header row makes 151 rows altogether. In terms of the algorithm, we're going to start by loading the iris.csv data into a JavaScript array, and then we're going to iterate over each line of this data, which is each row, where each row of values represents measurements for single iris flower. We're going to select two data measurements for each row, which we're going to call x and y, we'll use these to compute the coordinates to plot our point. We're also going to look at the final value in the row because this is the class or variety of iris through three different kinds of iris flower remember. Then we're going to use this iris variety value to set text color, probably red, green, or blue. Then once we've set the text color and we've worked out the x, y coordinates, we're going to plot a data point at the appropriate coordinates. We'll do this for each row of the data, so we'll have 150 data points if we go through the whole iris CSV file. That's the algorithm you're going to be implementing. Now, you do have enough information based on everything we've learned so far in the course to go and implement the thing now, and do feel free to do that. But if you want some more hints and tips, there's another video or two below that you can carry on watching, so you can get some more help putting together your implementation of this algorithm. Good luck. Thanks.
