Data Modelling & Analysis

How can data models be applied to real-life scenarios?

• By organizing and structuring data to represent real-world entities and relationships.

• By ignoring real-world entities and relationships

• By randomly assigning data without any structure

• By using data models only for theoretical purposes

How does Microsoft Excel support data analysis and visualization?

• Real-time collaboration features

• AutoSum function

• Conditional formatting

• Tools like Power Query, Power Pivot, and Power View

Explain how conditional formatting can be used in modelling tools.

• Conditional formatting can be used to play music in modelling tools

• Conditional formatting is used to create 3D models in modelling tools

• Conditional formatting can be used in modelling tools to visually highlight data points based on specified conditions, aiding in the identification of trends and outliers.

• Conditional formatting is only applicable to text formatting in modelling tools

What are the advantages of using Microsoft Access for databases?

• Limited storage capacity

• Incompatible with other software

• Complex user interface

• The advantages of using Microsoft Access for databases include user-friendly interface, easy integration with other Microsoft products, quick development, ability to create forms and reports easily, and suitability for small to medium-sized databases.

How do you set up a data model in a database?

• Identify entities, create tables with attributes, establish relationships

• Create tables without attributes, establish random relationships

• Establish relationships first, then identify entities

• Select random attributes, create tables without relationships

Why is collecting user data important in data modelling?

• User data is too expensive to collect

• User data is irrelevant for data modelling

• User data decreases the accuracy of data models

• User data enhances the quality and accuracy of data models.

What is the role of pattern recognition in computational thinking?

• Pattern recognition only applies to visual data in computational thinking

• Pattern recognition plays a crucial role in computational thinking by enabling the identification of patterns in data or problems to develop algorithms or solutions.

• Pattern recognition is used to create chaos in computational thinking

• Pattern recognition is irrelevant in computational thinking

How can you search for answers within a dataset effectively?

• Filter, sort, and query the dataset.

• Highlight, underline, and circle the dataset.

• Shuffle, categorize, and summarize the dataset.

• Sing, dance, and meditate with the dataset.

Why is it important to highlight important data in a model?

• Highlighting data is unnecessary and adds complexity to the model.

• It helps stakeholders focus on critical information and facilitates better decision-making.

• It confuses stakeholders and leads to poor decision-making.

• Stakeholders prefer to analyze unorganized data for better insights.

What are some popular software choices for data modelling methods?

• DataForge

• ER/Studio, ERwin Data Modeler, Oracle SQL Developer Data Modeler, IBM InfoSphere Data Architect

• DataCraft

• DataMaster