| Summary: | Learn: Create a module of the ML model for later use. Connect Python and SQL to transfer data from Jupyter to Workbench. Visualize data in Tableau. Analyze and interpret exercise outputs in Jupyter and Tableau. About: Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language. SQL is the most widely used means for communication with database systems. Tableau is the preferred solution for data visualization. The course starts off by introducing software integration as a concept. We discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints. Then we continue by introducing the real-life example exercise the course is centred around: the Absenteeism at Work dataset. The preprocessing part that follows will give you a taste of what BI and data science look like in real-life, on-the-job situations. Then we continue by applying some Machine Learning to our data. You will learn how to explore the problem at hand from a machine-learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it -- a truly comprehensive ML exercise. Connecting Python and SQL is not immediate; we show how that's done in an entire section of the course. By the end of that section, you will be able to transfer data from Jupyter to Workbench. And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together
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