Create a Data Analysis workflow in Orange is simple. We start with opening the data in the Far Widget. Some preloaded data sets are available, so let's select RIS and explore it. Let's check out the data in a data table. I will select the data table widget and connect the Far Widget to it. Here we have 150 RIS flowers from the famous Fischer's data set. Flowers are described by four features, the length and width of sepals and the length and width of petals. Each flower is labelled with one of the three classes, a species of RIS, RIS-Sytousa, RIS-Versiculla and RIS-Viginica. Now let's visualize the data. I'll connect distributions widget to the Far Widget. In this way, the Far Widget sends any data it loads to the distributions widget. In distributions, we can walk through all the features in the data. Petal length and petal width seem to nasty separate different species of RIS. We can additionally inspect the data in the scatter plot. The plot that we see is a bit messy. RIS-Versiculla in red and Viginica in green are not well separated. I wonder if there's any pair of features that would nasty separate the three classes. I can click Ranked Projections to score all feature pairs. A higher score indicates about a separation of different species of RIS. The best scored scatter plot, with petal length and petal width, really nicely separates data instances of different class. But there's some overlap of RIS-Versiculla and RIS-Viginica. I'll select data instances in the overlapping region. Scatter plot widget automatically sends the data to its output. Now I connect another data table to the scatter plot to inspect selected data instances. No surprises here. They are all either RIS-Versiculla or RIS-Viginica and seem to have similar values for all four features. We can expand the workflow with other widgets or save it for frequent use. But for now, that's it. We've learned that orange widgets communicate with one another and the changes in one widget are immediately propagated through the workflow.