Power BI's advanced charting features, including gauges, funnels, radar charts, calendars, and Sankey diagrams, elevate data visualization by presenting insights in innovative formats. These specialized visuals in Power BI empower users to convey intricate information effectively, enhancing their ability to derive valuable insights from diverse datasets. You will start this course by creating gauge charts and funnel charts. Next you will visualize data using Sankey diagrams and learn how to work with Gantt charts to map project schedules. Finally, you will explore how to use radar charts to compare multiple variables across data points and implement heatmaps to highlight patterns and variations. You will also work with calendar layouts, which are similar to heatmaps but showcase values within a calendar framework.

Power BI's data modeling capabilities empower users to craft sophisticated data relationships, calculations, and hierarchies. With the ability to establish meaningful connections between different data tables through relationships, users can seamlessly integrate disparate datasets and facilitate the creation of dynamic calculated columns and measures for in-depth analysis with Data Analysis Expressions (DAX). Hierarchies further enhance exploration by enabling intuitive drilldowns in your data. In this course, you will explore calculated tables and perform operations such as UNION, INTERSECT, and NATURALINNERJOIN to craft new tables. You will work with calculated columns and create and use calculated measures. Next, you'll learn how to create and model relationships between your tables, explore cardinality, and uncover the concept of cross-filter direction that influences the flow of filters across relationships. Finally, you'll explore hierarchies in Power BI, working with both default and custom hierarchies.

Power BI is a popular and widely used business intelligence and data visualization tool developed by Microsoft. It empowers organizations to transform raw data into meaningful insights and interactive visualizations, enabling more informed decision-making. You'll begin this course by getting a big picture understanding of what Power BI has to offer, including three core features: data connectivity for easy data loading, transformation and modeling options, and visualizations. Next, you'll install Power BI desktop from the Microsoft Store, and explore its user interface. You'll see where you can build your reports, view your data and set up modeling relationships. Finally, you will access data from a file and learn how to transform and process that data so it can be used for data analysis and visualizations. You will work with Power Query to rename column headers and remove duplicates, learn to merge and split columns, and view data profile, quality, and distribution. When you have your data in the right format, you'll load the data into Power BI and set up a simple bar chart visualization in your report.

Power BI offers robust capabilities to seamlessly retrieve data from various cloud storage services, facilitating efficient integration of cloud-based data into reports and visualizations. It supports connections to popular cloud platforms, including Microsoft Azure Blob Storage, Amazon S3, Google Cloud Storage. In this course, you will learn how to import data into Power BI from BigQuery on the Google Cloud Platform (GCP). You will create a service account with the permissions necessary to access and load data. Next, you will work with S3 buckets on Amazon Web Services (AWS) and create an Identify and Access Management (IAM) user with the credentials necessary to access and work with S3. You will write a Python script to connect to S3 and load and import data into Power BI. You will then access and retrieve data from Azure Blob Storage to load it into Power BI for analysis and use a connection string to authenticate and authorize Power BI to Azure storage. Finally, you will import data by making REST API calls and from embedded HTML in a web page using a Web connector and build visualizations such as pie charts, line charts, treemaps, and area charts in your Power BI report.

Power BI offers a seamless and efficient way to connect and retrieve data from databases. It supports a wide range of database systems including, but not limited to, SQL Server, Oracle, MySQL, and PostgreSQL. Users can establish connections to these databases by providing connection details such as server addresses, authentication credentials, and database names. You will start this course by loading files stored in SharePoint into Power BI. You will authenticate yourself to SharePoint from Power BI to access, process, and load data. Then, you'll visualize the data with pie and donut charts. Next, you'll load data from SQL Server into Power BI. You'll discover the difference between the Import mode and the DirectQuery mode. Lastly, you'll import data from a MySQL database, connect to MySQL using root credentials and user credentials, and learn to reset credentials and configure new ones.