[MUSIC] Welcome back. Because there are so many data visualization tools out there, it can get rather confusing. In this lesson, we're going to examine the types of technology tools used to do data visualization. We will be using the public version of Tableau for our course. But keep in mind the things you will learn in this course are very much software neutral. So it doesn't actually matter what piece of software you use. First, let's look at the apps that everyone has some passing familiarity with, which is Microsoft Excel. For the versions beginning in 2010, Microsoft included a tool called PowerPivot that allows advanced data connections and improved pivoting capabilities far beyond of what was on offer before in Excel, which was the standard pivot tables. If you don't see it immediately available in your version of Excel, check the add-ins and see if it's activated. There's a real learning curve for the tool called PowerPivot. But it is in fact a very powerful option for those who are tied into the Microsoft architecture, especially through SharePoint and PowerBI. If you don't have those products, then sharing it across teams will still have the same limitations as before with Excel, when you were using standard pivot tables. However, you have much improved data connections in ways that simply were impossible before. Secondly, when you're searching for data visualizations on the web, you often run across either Python or R as data visualization tools. For machine learning and big data, which we sort of defined in the last lesson, those are possibly good options. But I would strongly caution you to learn and become an expert in either Excel or Tableau before diving into Python or R. Even though both of this products are free, they are both incredibly difficult and overwhelming to learn if you're a beginner in this area. It will be very frustrating for you unless you're a strong programmer. That being said, both Python and R are in fact tools that are extremely powerful. And once programmed, can process unstructured data and big data much more quickly and efficiently than any other tool out there today. Finally, we come to Tableau's products. They're different flavors of the software, which can be confusing to a lot of people. So I'm going to go over them one by one so that you have a sense of them. First is Tableau Public. That's the software we're going to be using in the course. It's free but it has a notable limitations. First of all, all of the information, including your raw data are stored in the Tableau cloud. And can be downloaded by anyone, with no controls possible. That means you have to be extra careful with your data before it's saved. The other caveat is that the files cannot be saved locally. You have to save it to the cloud. If you have data that's not anonymized in any way, you'll have to prep it ahead of time in Excel or some other program before importing it into Tableau Public. Because once the data are imported into Tableau, it becomes a public document that can be downloaded by anyone. Tableau Desktop, on the other hand, which is not free, can connect to an amazing array of data sources and has very strong security for your data. It's the full version of Tableau. And you can get data from your own data sources, from your own databases, your own data warehouses, SQL Server, Google Analytics, Oracle, Amazon Web Services. Virtually any data source can be connected through Tableau Desktop. And it really is a very powerful tool if you can get a copy of that license. Tableau Server allows the most flexibility of all of the Tableau products. But it is only available to those who can support a server that is dedicated to Tableau. As such, there's a very large financial investment for an organization having this version. But if your organization does, count yourself lucky because you could harness the full capability of the software in ways that no other version can have. For example, you can control how others see your data, even when it's embedded on a public website. Then you can have more secure access to data and prevent people from downloading those data sets. And the final type of Tableau product is called Tableau Online. Tableau Online is intended to be a way to allow control of your data when embedding data visualizations. But it doesn't require a large investment of money into a server. The disadvantage of this approach is that in order to view the visualization online, the viewer needs a Tableau Online account as well. So visualizations produced with Tableau Online cannot be shared with the public like it can with Tableau Server or Tableau Public, unless you save it as a Tableau Public file. Whatever piece of software or app you choose, you can be assured that this course and this specialization can address your needs. So say tuned for the other parts of the class.
