INFORMATİON TECHNOLOGİES

Data can be referred as raw, unorganized facts and concepts which need to be processed. Information is organized, categorized, contextualized data. Data becomes meaningful with information. Information needs to be analyzed especially for making strategic decisions. Information does not need to be useful. Knowledge is simply defined as useful information. Wisdom is related with judging which knowledge is true and applying it to real life.
Data can be grouped as qualitative data and quantitative data. Quantitative data can be referred as data in numerical form whereas qualitative data can be referred as data different than numerical form. A quantitative or numeric data can be continuous or discrete. A continuous data can be any value from the specified range. A discrete data can be any one of the specified discrete numerical values. Qualitative data or categorical data can be grouped as nominal data and ordinal data. Unlike nominal data, categories are in certain orders in ordinal data.
Data can be collected explicitly or implicitly. Explicitly collecting data includes the ways which data is gained directly such as questionnaires, votes etc. Tracking user behaviors, keeping click through rate of a web site are some of the ways of implicitly collecting data.
Data is valuable when it is analyzed and used for gaining useful information. Thus, data needs to be managed in order to define, identify, describe, modify, destroy, mine, report data and so on. Data management includes topics such as data defining, data architecture management, data quality management, master data management, metadata management, database management, data warehousing, reporting and so on.
Extracting knowledge from data is extremely valuable since raw data is converted into useful, interesting form.

Qualitative data or categorical data can be grouped as nominal data and ordinal data. Unlike nominal data, categories are in certain orders in ordinal data.

Managing data is important and provides some advantages described as follows:
• It provides mechanisms to become data into accessible, reliable, authentic and complete form.
• Itprovides mechanisms to securely share data between information technology systems, applications, departments or employees for an enterprise.
• It helps decision makers to make strategic decisions by allowing mining and analyzing data.
• It allows organizing, tracking, deleting, creating, and reporting data.
• It disallows redundancy and loss in data.
• It minimizes damage caused by errors related with data.

Data warehousing keeps historical data for preprocessing, mining, analyzing, reporting data.

Requirements should be detailed, relevant, descriptor, unambiguous,
complete and conistent.

For a binary relationship, cardinality ratio can be one of the following
options: one-to-one (1:1), one-to-many (1:N), many-to-one (N:1) and many-to-many (N:M).

Data integration: Data integration merges data from different sources
such as databases, data flats etc. into a compatible data store.

Data mining is used with purpose of classification, clustering, mining frequent patterns and associations, predictions, outlier analysis and so on.

Metadata is generally referred as data about other data or catalog for a data. It describes, identifies, explains, manages, and uses a data resource.

A metadata repository of a data warehouse includes many information
about the data warehouse and some of them are listed below:
• Information about description, structure, security, authentication, usage statistics of the data warehouse
• Information about the data warehouse’s ownership
• Information about applied rules on data, such as transformation, cleaning, data mining, querying,
loading and etc.
• Information about structures of data sources of the data warehouse,
• Information about structures of data repositories and etc.

Data can be referred to as raw, unorganized facts and concepts which need to be processed. Information is organized, contextualized, meaningful data. Information is not necessarily useful in and of itself. Knowledge is defined as useful information, while wisdom is judging which knowledge is true and applying it to real life. The correct answer is C.

Data management allows for the organization, tracking, deleting, creating, and reporting of data, but does not necessarily ensure the transformation of data into knowledge through categorization. The correct answer is C.

Data warehousing keeps historical data for preprocessing, mining, analyzing and reporting. As a result, the correct answer is B.

The main steps for designing a database are; requirement collection and analysis, conceptual design, logical design, and physical design. Low level design is not one of the steps. The correct answer is E.

Requirement collection and analysis entails establishing requirements based on the intents and expectations of users, and implementing them as rules or constraints for the database. The others options are not correct. The correct answer is C.

Since each doctor in the hospital will have multiple patients, the entity relationship between the doctor and their patients would have a cardinality ratio of 1:N, one doctor for many patients. The correct answer is B.

Conceptual design provides the conceptual schema of a database by representing entities and relationships. Logical design is a more precise map of the entity relationships, providing a relational schema of the database. The correct answer is D.

Using the primary key of one entity and inserting it to the other entity types relation as a foreign key is one of the three possible approaches to mapping 1:1 binary relationship types. The correct answer is C.

DBMS stands for database management system, and Oracle, DB2, SQL Server and MySQL are all various DBMS products. The physical specifications and therefore physical design of the database may differ from product to product. The correct answer is A.

The second step of knowledge discovery, data preprocessing, uses various techniques such as data cleaning, data integration and data transformation to get rid of inconsistent, noisy, dirty and low quality data. That suggests that the correct answer is B.