DA-100: Analyzing Data with Microsoft Power BI

You will learn how to retrieve data from a wide variety of data sources, including Microsoft Excel, relational databases, and NoSQL data stores. You will also learn how to improve performance while retrieving data.

Power Query has an incredible amount of features that are dedicated to helping you clean and prepare your data for analysis. You will learn how to simplify a complicated model, change data types, rename objects, and pivot data. You will also learn how to profile columns so that you know which columns have the valuable data that you’re seeking for deeper analytics.

The process of creating a complicated data model in Power BI is straightforward. If your data is coming in from more than one transactional system, before you know it, you can have dozens of tables that you have to work with. Building a great data model is about simplifying the disarray. A star schema is one way to simplify a data model, and you’ll learn about the terminology and implementation of them in this module. You will also learn about why choosing the correct data granularity is important for performance and usability of your Power BI reports. Finally, you’ll learn about improving performance with your Power BI data models.

Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values. You can use DAX to solve a number of calculations and data analysis problems, which can help you create new information from data that is already in your model.

Data Analysis eXpression (DAX) language is a key feature of Power BI. It is used to create calculated columns, calculated tables, and measures. In this module, you will learn how to use DAX to solve typical analytics problems. You will learn about one of the most popular DAX functions, CALCULATE, and how it can override the default behavior of Power BI.

Learn how to choose from the exceptional visuals that Power BI makes available to you. Formatting visuals will direct the user’s attention to exactly where you want it, while helping to make the visual easier to read and interpret. You will also learn about how to use key performance indicators (KPIs).

Power BI helps you create vibrant, highly useful reports that form a cohesive, data-driven story. You’ll learn how to use buttons, bookmarks, and other navigation techniques. Additionally, you’ll learn how to integrate Power BI reports with other applications. Power BI visuals can interact with each other, letting the user see exactly which data is appealing to them. You’ll also explore Power BI report themes to create a unified reporting experience across all reports.

Microsoft Power BI dashboards are different than Power BI reports. Dashboards allow report consumers to create a single artifact of directed data that is personalized just for them. Dashboards can be comprised of pinned visuals that are taken from different reports. Where a Power BI report uses data from a single dataset, a Power BI dashboard can contain visuals from different datasets.

Paginated reports allow report developers to create Power BI artifacts that have tightly controlled rendering requirements. Paginated reports are ideal for creating sales invoices, receipts, purchase orders, and tabular data. This module will teach you how to create reports, add parameters, and work with tables and charts in paginated reports.

You will learn how to share reports and datasets to your users. Although this can be a simple process, when you think about testing reports before deployment, it can get more complex. For instance, what if you are changing a report that is currently in use? Do you need to let the users test the report before you overwrite it? This module will guide you to creating a deployment strategy that makes sense for you and your organization. Further, if you have ever wondered about where data on a report originated from, then you will want to learn about data lineage in Power BI.

In Power BI, you can build multiple reports from a single dataset. This means that if you change the dataset, all the reports will be updated with that change. It also means that you can do the work of cleaning and prepping data a single time, instead of repeatedly for each report. Once a dataset has been deployed, you’ll want to automatically refresh it, so the users see the most current data in their reports. By using the Power BI gateway, it’s possible to retrieve data from on-premise data sources, for instance a SQL Server that might be in your organization’s data center.

Row-level security allows you to create a single or set of reports that targets data for a specific user. For instance, you can create a single report where a salesperson can only see their individual sales transactions and not the transactions of someone else. RLS can be implemented using either a static or dynamic method and you will learn both here. You will also see how Power BI simplifies testing RLS in both Power BI Desktop and the Power BI service.

You will learn how to use Power BI to perform data analytical functions, how to identify outliers in your data, how to group data together, and how to bin data for analysis. You will also learn how to perform time series analysis. Finally, you will work with advanced analytic features of Power BI, such as Quick Insights, AI Insights, and the Analyze feature.

This module describes the AI capabilities of Power BI.

Would you like to explore the journey of a data analyst and learn how a data analyst tells a story with data? In this module, you will explore the different roles in data and learn the different tasks of a data analyst.

Learn what Power BI is, including its building blocks and how they work together.