In this learning path, the student is exposed to various ways to:

Row-level security (RLS) allows you to create a single or a set of reports that targets data for a specific user. In this module, you'll learn how to implement RLS by using either a static or dynamic method and how Microsoft Power BI simplifies testing RLS in Power BI Desktop and Power BI service.

Learn how to construct transactions to control the behavior of multiple Transact-SQL statements. You'll learn how to determine whether an error has occurred, and when to roll back statements.

This course offers a deep dive into addressing dataset incompleteness. From basic drop methods to intricate regression imputations, emerge equipped to tackle any missing data challenge with confidence.

Can we characterize intelligent behavior? Are there theoretical foundations on which Artificial Intelligence can be grounded?

In this course, you’ll learn how to use Pandas index objects for advanced data analysis. You'll learn how to create and use numerical, interval, period, categorical, date-time, time-delta, and multi-indexing in Pandas to retrieve and manipulate data.

In this course you'll learn how to leverage statistical techniques for working with categorical data.

In this course you'll learn how to perform inference using linear models.

In this course you'll learn techniques for performing statistical inference on numerical data.

Data ingestion is crucial in analytics. Microsoft Fabric's Data Factory offers Dataflows for visually creating multi-step data ingestion and transformation using Power Query Online.

Explore how Microsoft Fabric enables you to ingest and orchestrate data from various sources (such as files, databases, or web services) through dataflows, notebooks, and pipelines.

Discover how to use Apache Spark and Python for data ingestion into a Microsoft Fabric lakehouse. Fabric notebooks provide a scalable and systematic solution.