Explore the concept of data modeling in data warehousing, specifically focusing on star and snowflake schemas, and their benefits in organizing data for analysis.
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Explore the concept of data modeling in data warehousing, specifically focusing on star and snowflake schemas, and their benefits in organizing data for analysis.
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- Explain the concept of data modeling in data warehousing. What are star schemas and snowflake schemas, and how are they used?Describe the principles of data warehousing and how they relate to data modeling. What are the advantages of using a data warehouse in a large organization?What are the key considerations when modeling time-dependent data, such as temporal databases or event sourcing systems?
- Discuss the use of data cubes and multidimensional modeling for advanced analytics in data warehousing.Explain the concept of data modeling in the context of data warehousing, including the various modeling techniques and their purposes.How does data modeling support the development of effective Business Intelligence (BI) systems?
- Explain the meaning of business analytics and its role in generating value from data. Distinguish between model and modelling, and explain the steps in solving business problems using analytics.Explore the concept of data warehousing and how it relates to data modeling.Develop a simple data model for a student data- base that includes student contact data, student demographic data, student grades data, and student financial data. Determine the data attributes that should be present in each table, and identify the primary key for each table. Develop a complete Entity Relationship diagram that shows how these tables are related to one another.
- Over the past few decades, technological advances have led to a dramatic increase in the amount of recorded data. The use of smartphones, RFID tags, electronic sensors, credit cards, and the internet has facilitated the collection of data from phone conversations, emails, business transactions, product and customer tracking, business transactions, and web browsing. Data mining is the process of applying analytical techniques to large quantities of data to find hidden structures, patterns, and relationships among variables. Illustrate some applications of the data mining techniques in business are used in order to gain insight on customers and to uncover patterns to improve business processesWhat is differ from SMA (Simple moving average), WMA (Weighted moving average), SLR (Single linear regression), and ESM ( Exponential smoothing model).What is producivity analysis? What is prescriptive data analytic model? How are the two related or how are they different? Give example of each.