DP-203: Data Engineering on Microsoft Azure
In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.
DP-100: Designing and Implementing a Data Science Solution on Azure
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
Three day
PL-300: Microsoft Power BI Data Analyst
This course covers the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution.
Three days
AI-900T00: Microsoft Azure AI Fundamentals
This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.
DP-300T00: Administering Microsoft Azure SQL Solutions
This course provides students with the knowledge and skills to administer 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.
Boot Camp: Power BI from Novice to Certified
Five days, extended hours each day.
Combination of SQL for Data Analytics and PL-300 Microsoft Power BI Data Analyst. Course PL-300 is the primary preparation tool for the Microsoft Certified: Power BI Data Analyst Associate certification exam, but requires a prequiste knowledge of both query techniques and relational data structures such as the star schema. SQL for Data Analytics provides this prequisite knowledge, so that students can make the most of PL-300 and maximize exam preparedness while enhancing job skills.
SQL for Data Analytics
SQL for Data Analytics
This course is designed as an introductory course for data scientists and data analysts who need to master Microsoft’s data analytics tools and platform. At the end of the course, you’ll be prepared to implement TSQL for data retrieval and analysis, and be well-prepared for further coursework requiring this knowledge, such as courses for Azure Synapse Analytics, Azure Data Bricks, Azure Machine Learning, and Power BI.
Our goal is to help you efficiently achieve your learning and skill goals. We hope you’ll find this Cuban Sandwich Press course to be the best technical training course you’ve ever taken!
While based on sound principles informed by instructional science and years of experience, it’s really very easy to understand. My goal, in the end, is to have you leave the class with a robust mental model of TSQL and its use within Microsoft’s analytical platforms. Having a mental model of the system means having enough conceptual understanding of what’s happening and why, so that your hands-on development of TSQL code to solve real-world analytical problems can happen accurately and efficiently once you’re back on the job solving real-world problems.
The course design takes a hands-on, project-based approach to learning and helping you build that mental model. This means that both conceptual content and hands-on exercises are oriented toward solving real-world analytical problems within a learning environment that simulates real-world systems to the extent manageable by students.
Learning Goals:
- Understanding database queries within analytical/reporting application architectures
- Basic database concepts for data analysts
- Creating Azure SQL databases
- Query development tools such as SQL Server Management Studio
- A deep dive into the SELECT query, including (but not limited to) joins, subqueries, derived tables, common table expressions, and views
- Implementing a data model as a star schema using views
- Creating an Azure Synapse Analytics workspace and running SQL queries against large text files using serverless SQL pools
- Using SQL in the process of Importing, combining, and visualizing data from an Azure SQL database and an Azure Synapse Analytics Serverless SQL pool in Power BI
The course is composed of six content modules. Each module contains four items in the course materials section of the Cubansandwichpress.com Web site:
- .pdf file which serves as the written courseware or book on which everything else is based
- Pre-recorded video of an annotated presentation on the module’s topics
- .pdf file containing hands-on lab instructions
- Video demonstration of how to successfully complete the hands-on lab
In addition to the above, students attend a live, instructor-led presentation followed by a question & answer session. Each instructor-led session is followed by a hands-on lab session. An instructor is available during the hands-on lab sessions to answer questions, provide coaching, etc.
SQL for Data Analytics (2 day, extended hours)
2 days, extended hours each day.
This course is designed as an introductory course for data scientists and data analysts who need to master Microsoft’s data analytics tools and platform. At the end of the course, you’ll be prepared to implement TSQL for data retrieval and analysis, and be well-prepared for further coursework requiring this knowledge, such as courses for Azure Synapse Analytics, Azure Data Bricks, Azure Machine Learning, and Power BI.
Our goal is to help you efficiently achieve your learning and skill goals. We hope you’ll find this Cuban Sandwich Press course to be the best technical training course you’ve ever taken!
While based on sound principles informed by instructional science and years of experience, it’s really very easy to understand. My goal, in the end, is to have you leave the class with a robust mental model of TSQL and its use within Microsoft’s analytical platforms. Having a mental model of the system means having enough conceptual understanding of what’s happening and why, so that your hands-on development of TSQL code to solve real-world analytical problems can happen accurately and efficiently once you’re back on the job solving real-world problems.
The course design takes a hands-on, project-based approach to learning and helping you build that mental model. This means that both conceptual content and hands-on exercises are oriented toward solving real-world analytical problems within a learning environment that simulates real-world systems to the extent manageable by students.
Learning Goals:
- Understanding database queries within analytical/reporting application architectures
- Basic database concepts for data analysts
- Creating Azure SQL databases
- Query development tools such as SQL Server Management Studio
- A deep dive into the SELECT query, including (but not limited to) joins, subqueries, derived tables, common table expressions, and views
- Implementing a data model as a star schema using views
- Creating an Azure Synapse Analytics workspace and running SQL queries against large text files using serverless SQL pools
- Using SQL in the process of Importing, combining, and visualizing data from an Azure SQL database and an Azure Synapse Analytics Serverless SQL pool in Power BI
The course is composed of six content modules. Each module contains four items in the course materials section of the Cubansandwichpress.com Web site:
- .pdf file which serves as the written courseware or book on which everything else is based
- Pre-recorded video of an annotated presentation on the module’s topics
- .pdf file containing hands-on lab instructions
- Video demonstration of how to successfully complete the hands-on lab
In addition to the above, students attend a live, instructor-led presentation followed by a question & answer session. Each instructor-led session is followed by a hands-on lab session. An instructor is available during the hands-on lab sessions to answer questions, provide coaching, etc.
