Power BI includes power features that offer valuable insights into your data. The Key Influencer visual helps identify the factors or attributes that have the most significant impact on a chosen outcome. On the other hand, the Decomposition Tree provides an intuitive way to break down complex data into understandable components, allowing users to explore data hierarchies and identify key contributors to a specific metric. You will start this course by learning how Power BI will leverage artificial intelligence (AI) for detecting anomalies in time series data. Next, you will use decomposition tree visualizations to drill-down into measures. You will also make use of AI splits, which are AI-generated drill-downs into high value and low value categories. Finally, you will use Key Influencer visualizations to help you identify factors in your data that drive specific outcomes.

The Power BI Fabric administrator is a role dedicated to managing the Power BI service for an organization. Fabric admins have the authority to control workspace settings, access permissions, and data policies, ensuring secure and efficient data governance within the Power BI environment. In this course, you will learn about the administrative roles in Power BI. You'll see that the Fabric administrator role is the main administrative role and has full access to management tasks, usage auditing, and performance monitoring. You will work in the Admin Portal, which is the primary workplace of Fabric administrators, where all administration settings, audit logs, and performance metrics are visible. Next, you will learn how to use Power BI as a Fabric administrator. You discover how to allow or block specific operations for all users in an organization or for specific security groups. Finally, you will explore a number of administrative settings and how you can configure them.

Power Query is a powerful data transformation and preparation tool, seamlessly integrated into Microsoft Power BI and other Microsoft products such as Excel. It enables users to effortlessly extract, transform, and load data from various sources, ensuring data consistency and quality. Begin this course by exploring how to transform and clean data using Power Query. You will learn to delete, rename, and move columns, remove duplicates, and perform a host of other data cleaning and processing operations. Next, you will discover how to create query duplicates and references and how to perform basic and advanced filtering on your data. Then you will find out how to add columns with calculated and conditional values and how you can use merge queries to create a new column in your data. Finally, you will perform grouping and aggregation operations, create parameterized queries, and combine query data using the append operation. This course will provide you with a solid understanding of how Power Query can be used for data transformation.

Power BI service is a Microsoft cloud-based platform designed for sharing, collaborating, and publishing Power BI reports and dashboards. Begin this course by exploring dataflows and datasets in order to create, use, and refresh dataflows and datasets in the Power BI service. Next, you will create and use auto-generated reports and manually create reports and dashboards in the Power BI service. You will also learn how to use the dashboard Q&A feature to ask natural language questions to Power BI. Then you will examine a variety of features of reports and dashboards and find out how to connect to a dataflow from Power BI Desktop to create a report using that data. Finally, you will focus on publishing reports from the desktop to the Power BI service, creating a dashboard with charts from multiple reports, using slicers to filter the data in your visuals, and exporting and embedding Power BI reports in SharePoint sites. Upon course completion, you will be able to confidently use Power BI service to create and collaborate on reports and dashboards.

Power BI's artificial intelligence (AI) capabilities offer a remarkable blend of data analytics and artificial intelligence. Power BI empowers users to unlock hidden patterns, generate predictive forecasts, and gain a deeper understanding of their data without requiring extensive data science expertise. You will start this course by generating insights on data and reports. You will explore how to generate these insights on datasets, reports, and even dashboard tiles. You'll also learn how to harness AI to explain fluctuations in your data. Next, you will learn how to perform text analytics in Power BI. You will explore how to use the Text APIs for language recognition, key phrase extraction, and sentiment analysis. Finally, you will use image analytics in Power BI by loading a dataset of images and generating tags for each set.

In Power BI, you can establish data relationships and use Power Query to perform joins to merge multiple data tables into a cohesive dataset. By defining these relationships and using functions like merge queries, you can seamlessly combine data from different sources based on common keys, enhancing data integration and enabling in-depth analysis. In this course, you will discover how to create relationships between tables that will allow you to perform more complex inter-table operations. Next, you will learn how to perform SQL-style joins on two tables. Power BI offers numerous types of joins, including inner, outer, left, right, left anti, and right anti joins. You will implement all of these join techniques to define the differences between them and describe their use cases. You will also learn how to pivot and aggregate data in Power Query. Finally, you will explore fuzzy merging to perform joins for inexact matches. This course will provide you with a foundation for how to perform exact and fuzzy joins in Power BI.

Smart narratives and the Q&A feature in Power BI enhance data storytelling and interactivity for reports and dashboards. Smart narratives automate the creation of textual descriptions and insights from data. The Q&A feature, on the other hand, allows users to ask natural language questions about their data and receive immediate responses and insights. You will start this course by using smart narratives in Power BI to generate natural language summaries of visualizations, dashboards, and reports. You will configure dynamic variables in your narratives that update with your data. Next, you will use the Q&A feature to ask natural language questions about visualizations that will be answered with numbers, charts, or tables. You will also learn to use the Teach Q&A feature to teach the Q&A widget to work with organization-specific terminology. Finally, you will learn how to use Power BI Copilot. You will explore how to calculate measures using natural language queries to generate DAX commands and add those measures to your reports.

Machine learning (ML) in Power BI provides a dynamic and transformative dimension to data analysis. This feature enables users to create predictive models, discover patterns, and make data-driven decisions while requiring no programming or machine learning expertise. You will start this course by creating a binary classification model that will be used to predict categorical variables. You will load in data and perform exploratory data analysis, including visualizing the data and extracting insights. Then, you will create and train a binary classification model and use it for predictions. After training the model, you will view the training report that will allow you to evaluate your model's performance. Next, you will build a regression model to predict the value of a continuous variable. You will load in the data and perform preprocessing steps to clean your data. You will also perform exploratory data analysis and visualize your data to extract insights about patterns and trends in your data. Then, you will create and train your regression model and learn how to interpret the training report generated by Power BI. Finally, you will perform multi-class classification.

Power BI Mobile is a dedicated app that allows users to access their Power BI reports and dashboards from smartphones and tablets. It offers a responsive and user-friendly interface, enabling mobile data exploration, sharing, and collaboration. Begin this course with an exploration of the Power BI Mobile application on an Android device, learning how to create a mobile layout for a report and add visual-level and report-level data. Next, you will examine the features of the Power BI Mobile application to see what functionality is available to you on the go. Then you will learn how to configure a shortcut to open a report from your phone's home screen and how to create and use QR codes to easily share a report. Finally, you will discover how to share dashboards with other team members and how to use annotations to mark important visuals in a dashboard. After completing this course, you will be able to confidently use the Power BI Mobile application to view and analyze reports and dashboards on your mobile device.

Power BI's real-time streaming capabilities enable users to visualize and analyze data as it's generated or updated. By connecting to various streaming data sources, such as Internet of Things (IoT) devices or social media feeds, Power BI allows for immediate insights and dynamic dashboard updates, enhancing situational awareness and enabling proactive decision-making based on the most current information. In this course, you will learn how to connect to on-premises data sources using a data gateway. You will explore the two types of data gateways: standard mode and personal mode. Next, you will learn about the three main types of Power BI datasets: streaming, PubNub, and push datasets. You will discover how to create and use streaming dashboards, create dashboards with streaming tiles and different visuals, and configure live dashboard tiles. Finally, you will explore how to create hybrid datasets, which are both streaming and push, and how to create reports using this data. You will also learn about other advanced features useful for data streaming, such as dashboard alerts, scheduled refresh of data, and query caching.

In Power BI, a scorecard is a useful visual tool for presenting and tracking essential key performance indicators (KPIs), offering a quick and concise overview of critical data points. On the other hand, paginated reports are structured documents designed for generating formal, print-ready reports. Power BI's deployment pipelines play a pivotal role in streamlining the development and deployment of BI solutions and automating processes. In this course, you will discover how to use scorecards and metrics to track KPIs. Then you will learn how to pin visuals as live tiles and update them by refreshing data. Next, you will find out how to add slicers to filter visualizations and explore how to create and embed paginated reports in your dashboard. Finally, you will examine Power BI applications and deployment pipelines, create Power BI apps to package your reports and dashboards, and use deployment pipelines to manage the life cycle of your content in the Power BI workspace. Upon course completion, you will be able to effectively work with scorecards, dashboards, and other important features of Power BI.

Some of Power BI's advanced capabilities include mapping relationships among tables and customizing the look and functionality of matrix visualizations. You'll start this course using E-R (entity-relationship) diagrams to create, edit, and delete relationships among columns in different tables. You'll then move on to creating matrix visualizations and setting up a matrix control. You'll use the matrix control to visualize parent and child columns via a drill-down feature. You'll then create a column from a formula (known as a measure in Power BI) and perform various formatting operations to your matrix visualization, such as adding a title and row subtotals, editing text size and alignment, and turning on vertical and horizontal gridlines. Lastly, you'll implement conditional formatting on your matrix visualization.

Power BI is one of the most commonly-used tools for building data reports from various source data. In this course, you'll start by installing Power BI Desktop and importing data to it from several sources, including the relational databases, MySQL and PostgreSQL, cloud data warehouses, and Excel, CSV, JSON, and XML files. You'll then use Power BI to perform simple operations, such as changing data types, renaming columns, and using uppercase text. You'll also open and run the Power Query app. Moving on, you'll use Power BI to create and format simple data visualizations, such as bar charts, line charts, and stacked bar charts. Finally, you'll practice aggregating numeric data and string (categorical) data in charts, working with the correct aggregation operators for their respective fields.

Treemaps are an impressive hierarchical data visualization feature of Power BI. Add in tooltips, matrices, slicers, and mobile readiness, and your reports will be highly interactive and engaging. You'll start this course by creating and configuring a treemap in Power BI. You'll then set up a treemap tooltip and configure it to use a chart on another Power BI page. Next, you'll create a Power BI report that automatically links the treemap to a matrix visualization. You'll then optimize this report for mobile device viewing. You'll move on to use slicers and add them to a page in your Power BI report. Lastly, you'll work with different slicer settings for numeric and string (categorical) fields and implement horizontal and vertical slicer layouts.

Power BI and Power Query can be used in combination to facilitate both basic and advanced operations. In this course, you'll learn how to use Power Query to create the two types of calculated columns, custom and conditional. Next, you'll use Power BI to import and clean data and create stacked line charts (area charts) to visualize data. You'll filter the unique categorical values of the visualized series using various filtering techniques. You'll then combine data between tables using a foreign key relationship and create a bar chart with line markers using the analytics card. You'll move on to import a large number of files residing in the same folder. Lastly, you'll use Power BI's UI and Advanced Editor to perform basic data cleaning operations and Power Query to perform various advanced operations.