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We're here to help you find itThe Data Analysis with Multidimensional Modelling course is designed to equip learners with the skills to utilize business intelligence (BI) tools and data modelling techniques. In Module 1: Introduction to Business Intelligence and Data Modelling, students will gain foundational knowledge of BI concepts and data models, understand the differences between MS SQL databases and cubes, and learn how to connect databases with SSIS and SSAS. They'll explore the creation and connection of data sources and multidimensional databases.
Module 2: SCD and Dimensions with Cube dives into practical aspects, teaching students to work with dimensions, measures, and measure groups, and introduces MDX for cube calculations.
In Module 3: Tabular Data Model with Analysis Services, learners will create a tabular data model and employ DAX to formulate calculated columns and measures. This course will be instrumental for those seeking to enhance their data analysis capabilities and gain proficiency in multidimensional modelling and business intelligence tools.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To ensure that participants can fully benefit from the Data Analysis with Multidimensional Modelling course, the following prerequisites are recommended:
These prerequisites are meant to provide a foundation for the technical content of the course. They are not meant to deter anyone with a genuine interest in learning. If you are new to some of these concepts, we encourage you to take introductory courses or seek out resources that can help you get up to speed. Koenig Solutions is committed to supporting you on your learning journey.
This course offers an in-depth understanding of business intelligence, data analysis, and multidimensional modeling, tailored for professionals in the data sphere.
Gain a comprehensive understanding of business intelligence, multidimensional modeling, and data analysis. Learn to create and manipulate data models, understand cubes, and use SSIS and SSAS with SQL databases.
Business Intelligence (BI) tools are software applications used to analyze an organization's raw data. BI tools are essential for companies to gain insights from their data, enabling them to make strategic and tactical business decisions. They function by collecting, processing, and analyzing large amounts of data to produce comprehensive reports and visualizations. These tools help businesses measure performance, monitor trends, and identify improvement opportunities, ultimately enhancing efficiency and profitability. They are widely utilized in various industries, including finance, healthcare, and marketing, to identify ways to increase productivity and reduce costs.
MS SQL databases are structured systems used to store, retrieve, and manage data efficiently. They support complex querying and data analysis. Cubes in MS SQL are part of a data structure that allows data to be analyzed from multiple perspectives quickly. These cubes are used in business intelligence applications to enable deep data analysis and visualization. Together, these tools are powerful for supporting large-scale data analytics projects, making them applicable in various industries like finance, healthcare, and marketing.
Data modeling techniques involve designing a data system's blueprint to organize data effectively, which aids in understanding complex data structures and relationships. Key techniques include relational modeling, using tables and relations to represent data and its interconnections; dimensional modeling, which focuses on efficiency in retrieving large quantities of data particularly for analytics, and simplifies data into measurements and contexts; and object-oriented modeling, which structures data as objects (similar to real-world entities) that interact with one another. Mastery in data modeling enhances database functionality, crucial for sectors relying on precise data analysis and management.
Data sources are the origins of information used for analysis, like databases, spreadsheets, or web resources. Multidimensional databases, on the other hand, are structured to make data analysis and query processing quicker and more efficient. They organize data into multi-dimensional arrays, ideally suited for complex queries and data analytics that need aggregation over several dimensions. This structure is particularly beneficial in environments like data analytics bootcamps online or online bootcamps for data science, where quickly accessing and analyzing vast arrays of data is critical for learning and applying data analysis techniques effectively.
Dimensions, measures, and measure groups are fundamental concepts in data analysis. Dimensions are categories or ways to split data, like location or time, making data meaningful and easier to understand. Measures are data metrics, such as sales or costs, calculated from datasets to quantify performance or behavior. Measure groups are collections of related measures stored together in a database, simplifying data management and analysis. Together, these elements help structure data effectively, enabling more insightful and powerful data analytics.
Multidimensional modeling is a technique used in data analysis to organize data in multiple dimensions, making it easier to analyze and extract valuable insights. This method structures data into cubes, which contain measured facts categorized by dimensions such as time, location, or product type. It's especially useful in scenarios where quick response times for complex queries are crucial, such as in financial analysis or inventory management. This modeling approach enhances data visualization and supports deeper analysis, crucial for data-driven decision-making in businesses.
A Tabular Data Model organizes data into a simple, spreadsheet-like format consisting of rows and columns. This model is quite similar to tables in a database or a spreadsheet in software like Excel. Each row represents a record with multiple attributes or pieces of data, and each column holds the data for one specific attribute or field. This straightforward structure makes it easy to observe relationships, manage data consistency, and efficiently query large sets of data. Essential for setting a clear foundation for data analysis, it's widely used in data analytics to simplify complex information into an understandable format.
This course offers an in-depth understanding of business intelligence, data analysis, and multidimensional modeling, tailored for professionals in the data sphere.
Gain a comprehensive understanding of business intelligence, multidimensional modeling, and data analysis. Learn to create and manipulate data models, understand cubes, and use SSIS and SSAS with SQL databases.
