Organizations that rely on instinct often miss key opportunities. The ones that trust their data? They win more customers, keep them longer, and earn more in the process. In fact, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable.

This course shows you how to use Microsoft Excel to make sense of data and communicate it clearly. You’ll learn how to organize, clean, and analyze datasets, build dashboards that update with a click, and present insights using tools like PivotTables, Power Pivot, and geospatial charts. For professionals responsible for reporting, forecasting, or decision support, this training delivers practical, real-world skills you can apply right away.

In the future, organizations that succeed will be those that put data at the forefront of their business strategy. This Data Literacy training is designed to give you a high level overview of the key topics in Data Science and Machine Learning. Designed exclusively for students who want to learn about the basics of data science and machine learning at a high level, without needing to learn how to code or cover complex mathematics. In this course you’ll learn the fundamental concepts relating to data, allowing you to understand what makes data suitable for data analysis, visualization and machine learning. Then we’ll give you a quick overview of important statistical topics, such as mean, standard deviation, and the normal distribution. Afterwards you will learn the different ways data scientists are able to visualize data to convey their ideas in a clear manner. We’ll also teach you about the machine learning process, acquiring data, cleaning data, and an overview of the train/test split philosophy that supervised learning adheres to. Then we’ll show you some examples of regression and classification algorithms, as well as how to evaluate their results. We’ll also explore what the future holds by taking a peek at the bleeding edge of AI and ML, including DALLE-2 and GPT-3!

This course will teach the basics of Microsoft’s dialect of the standard SQL language: Transact-SQL. Topics include both querying and modifying data in relational databases that are hosted in Microsoft SQL Server-based database systems, including: Microsoft SQL Server, Azure SQL Database, Azure Synapse Analytics, and Microsoft Fabric.

This course provides students with the knowledge and skills to create a SQL Server database infrastructure for cloud, on-premises and hybrid relational databases and who work with the Microsoft PaaS relational database offerings. Additionally, it will be of use to individuals who develop applications that deliver content from SQL-based relational databases.

This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You’ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you’ll get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables.   You’ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you’ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.

Azure Databricks is a fully managed, cloud-based data analytics platform, which empowers developers to accelerate AI and innovation by simplifying the process of building enterprise-grade data applications. Built as a joint effort by Microsoft and the team that started Apache Spark, Azure Databricks provides data science, engineering, and analytical teams with a single platform for big data processing and machine learning. In this course, you’ll learn how to use Azure Databricks to train and deploy machine learning models.

This course teaches developers to utilize Azure Cosmos DB for NoSQL API and SDK. Students will learn query execution, resource configuration, SDK operations, and design strategies for non-relational data modeling and data partitioning.

This learning path prepares you for the task of developing data-driven applications by using Microsoft Azure SQL Database.

You’ll learn how to create and configure an Azure SQL Database, build and deploy database projects using GitHub Actions and Azure Pipelines, and automate the publishing process. Additionally, you’ll explore how to use Data API builder for Azure SQL Database and develop a data API with Azure Web Apps and Static Web App.

Furthermore, you’ll gain skills in importing data via an external REST endpoint, exporting data using an Azure Function, and securing an Azure SQL Database. These essential skills will empower you to effectively develop and manage applications using Azure SQL Database.