This course covers creating and orchestrating ETL pipelines using the industry’s best practices and tools: Python, SQL, Apache Spark, and Apache Airflow.

Learn how to transform data using Transact-SQL.

Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.

Create cohesive, inclusive, and efficient Power BI reports to effectively communicate results.

Explore key concepts of data analytics, including types of analytics, data, and storage. Explore the analytics process and tools used to discover insights.

Scalable data models enable enterprise-scale analytics in Power BI. Implement data modeling best practices, use large dataset storage format, and practice building a star schema to design analytics solutions that can scale.

An introduction to data science with no coding involved.

This course teaches how to build deep learning models using TensorFlow to predict rare probability events.

Like most developers, you probably have no idea what probabilistic data structures are. In fact, I did a super-scientific poll on Twitter and found that of 119 participants, 58% had never heard of them and 22% had heard the term but nothing more.

In this course, you will learn about Series, DataFrames, and indexes in pandas to help you efficiently manipulate and analyze data.

Apache Spark is a core technology for large-scale data analytics. Microsoft Fabric provides support for Spark clusters, enabling you to analyze and process data in a Lakehouse at scale.

Learn how to use functions in Transact-SQL, and how to group aggregated results.