Implementing Data Models and Reports with Microsoft SQL Server (MS-20466)
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About this course:
Our IT Ops training program is one-of-a-kind and offered for those wanting to create a powerful career in SQ; server solutions. The purpose of this course is to assist students in learning and practicing the art of managing BI solutions. What’s more is that our professional educators have vast Microsoft SQL server experience.
Students will learn to efficiently deliver reports using MS SQL server and its various tools. What’s more is that students will get their hands on live exercises by our accredited professionals to help them with better learning of Microsoft SharePoint and many other BI tools utilizing data mining.
Note: If you are into learning and practicing MS SQL Server, this course is ideal for you. Individuals’ part of this online course will be learning all the features and functions of Microsoft SQL Server 2012 and 2014. This course also prepares the students for the Microsoft 70-466: Implementing Data Models and Reports with Microsoft SQL Server certification exam.
After completing this course, students will be able to:
- Describe the components, architecture, and nature of a BI solution.
- Create a multidimensional database with Analysis Services.
- Implement dimensions in a cube.
- Implement measures and measure groups in a cube.
- Use MDX Syntax.
- Customize a cube.
- Implement a Tabular Data Model in SQL Server Analysis Services.
- Use DAX to enhance a tabular model.
- Create reports with Reporting Services.
- Enhance reports with charts and parameters.
- Manage report execution and delivery.
- Implement a dashboard in SharePoint Server with PerformancePoint Services.
- Use Data Mining for Predictive Analysis.
The Microsoft SQL Server training program is designed for students eager to analyze business tool and data as BI developers. Primary responsibilities include:
- Implementing analytical data models, such as OLAP cubes.
- Implementing reports, and managing report delivery.
- Creating business performance dashboards.
- Supporting data mining and predictive analysis.
This course requires that you meet the following prerequisites:
- At least 2 years’ experience of working with relational databases, including:
- Designing a normalized database.
- Creating tables and relationships.
- Querying with Transact-SQL.
- Some basic knowledge of data warehouse schema topology (including star and snowflake schemas).
- Some exposure to basic programming constructs (such as looping and branching).
- An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.
Frequently Asked Questions
This training will help you in your Job’s current role by learning and getting hands on experience in the following areas:
- Learn how Microsoft R Server and Microsoft R Client work. You will also learn how to write scalar functions.
- Understand the concept of ScaleR data sources, how data is read from XDF object and how to summarize data in XDF object.
- You will get hands on experience by reading a CSV file into XDF file, Transforming data on input, Reading data from SQL Server into an XDF file and Generating summaries over the XDF data.
- You will learn how to visualize data using plots and graphs specifically to in-memory and big data. How to use ggplot2 to visualize data in-memory and rxLine Plot , and rxHistogram to visualize Big Data.
- Learn how to use rExec to maximize resource use a PEMA class after creating it.
- Understanding of what Regression Models are and how you can build them from Big Data. You will learn creating clusters, regression models, how to produce data for making predictions and by using the models how you can predict the results by comparison.
- Learn how to create and score partitioning models generated from big data.
- Learn how to process Big Data in SQL Server and Hadoop by using transformation and cleaning big data sets approaches.
What do you understand by linear regression?
- Linear regression helps in understanding the linear relationship between the dependent and the independent variables.
- Linear regression is a supervised learning algorithm, which helps in finding the linear relationship between two variables.
- One is the predictor or the independent variable and the other is the response or the dependent variable
- In Linear Regression, we try to understand how the dependent variable changes w.r.t the independent variable.
- If there is more than one independent variable, then it is called simple linear regression, and if there is more than one independent variable then it is known as multiple linear regression.
Why is R useful for data science?
- R turns otherwise hours of graphically intensive jobs into minutes and keystrokes.
- In reality, you probably wouldn’t encounter the language of R outside the realm of data science or an adjacent field.
- It’s great for linear modeling, nonlinear modeling, time-series analysis, plotting, clustering, and so much more.
- To Summarize, R is designed for data manipulation and visualization, so it’s natural that it would be used for data science.
Practice Certification Exam
|Subjects||IT Ops & Management|
|Learning Style||Virtual Classroom|
|Course Duration||5 Days|
|VPA Discount||VPA Discount|