Analysis Services is a combination of two analytical engines (VertiPaq for multidimensional cubes and Data Mining and xVelocity for Tabular Data Models) used in decision support and business analytics. It provides enterprise-grade semantic data model capabilities for business intelligence (BI), data analysis, and reporting applications such as Power BI, Excel, Reporting Services, and other data visualization tools. This course provides a full introduction to the use of both of these engines in an enterprise environment.
The third major component of the course is a form of Machine Learning called Data Mining. Data Mining is the analysis of large data set to reveal hidden patterns and trends. This course will teach you how to create a Data Mining solution, how to compare and validate different data mining options (models) for a given data set, and how to use your chosen model in a consumer-friendly client.
The course is led by James Ring-Howell, a Microsoft Certified Trainer, Developer, and Database Expert. James is a full-stack developer with over 40 years of experience, and he has developed applications across all major industries, for Fortune 100 companies as well as local small businesses
The course is delivered online, and you can access it anytime with the All Access Monthly Subscription. The subscription not only gives you access to this course but also to over 2,500 hours of on-demand content. You can start a 7-day free trial with no obligation, and you have the option to cancel anytime.
In 1996, Microsoft entered the OLAP Server industry after acquiring Panorama Software’s OLAP software technology. The technology was later renamed to Analysis Services in 2000 when data mining services were added. Analysis Services 2000 saw several improvements, including a more flexible dimension design and an advanced calculation engine. Microsoft Analysis Services released the Unified Dimensional Model (UDM) in 2005 as a major update. This model supports MOLAP, ROLAP, and HOLAP all in one place, meaning it remains neutral in the ongoing debate between MOLAP and ROLAP in OLAP products.
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