Print| S NO | Assessment statement | Grade | Teacher’s notes |
| 1 | Describe the characteristics of different database models. | Database models should include:
Students will be expected to refer to actual examples in their descriptions. |
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| 2 | Evaluate the use of object-oriented databases as opposed to relational databases. | This may include references to data definition, manipulation and integrity. | |
| 3 | Define the term data warehouse. | Subject oriented, integrated, timevariant and non-volatile collection of data used in decision-making. | |
| 4 | Describe a range of situations suitable for data warehousing. | For example, strategic planning,business modelling. | |
| 5 | Explain why data warehousing is time dependent. | Data in a warehouse is only valid for a period of time. | |
| 6 | Describe how data in a warehouse is updated in real time. | Data is refreshed from data in operational systems. | |
| 7 | Describe the advantages of using data warehousing. | A single manageable structure to support decision-making. Allows complex queries to be run across a number of business areas. | |
| 8 | Explain the need for ETL processes in data warehousing. | Students should understand that processes are necessary to Extract data from disparate sources,Transform the data into a uniform format for specialized processing and Load the extracted data into the data warehouse. | |
| 9 | Describe how ETL processes can be used to clean up data for a data warehouse. | Examples should be used to show how disparate data can be changed to a uniform format in order to be suitable for analysis. | |
| 10 | Compare the different forms of discovering patterns using data mining. | Students are expected to be able to describe the conceptual approach used by:
The student does not need to understand the detailed implementation of these methods. |
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| 11 | Describe situations that benefit from data mining. | Examples can be cited such as the use of mining techniques by banks to identify fraudulent credit card use;retailers can use mining techniques to identify subsets of the population likely to respond to a particular promotion. | |
| 12 | Describe how predictive modelling is used. | The use of classification techniques such as “decision tree induction” or “backpropogation in neural networks”. The determination of values for rows of a database useful for predictions. | |
| 13 | Explain the nature of database segmentation. | The partitioning of a database according to some feature in common in the rows. | |
| 14 | Explain the nature and purpose of link analysis. | The use of rules to establish associations between individual records in a data set. | |
| 15 | Describe the process of deviation detection. | The detection of outlying data can be subjected to statistical techniques in order to identify unusual events or data subsets. |