Data Principles week 3 part 1

What is the main objective of Phase 3: Model Planning in the Data Analytics Lifecycle?

• To finalize the data analysis report

• To determine the methods, techniques, and workflow for model building

• To collect new data for analysis

• To deploy the final model into production

When can the data science team move to the next phase in the Data Analytics Lifecycle?

• After collecting all the data

• Once they have a good idea about the type of model to try and have refined the analytics plan

• As soon as the data is cleaned and prepared

• After the model has been deployed

What does the data science team do in addition to planning the model in Phase 3?

• They deploy the model to production

• They explore the data to learn about relationships between variables

• They communicate the results to stakeholders

• They prepare the data for analysis

Which phase follows Model Planning in the Data Analytics Lifecycle?

• Data Prep

• Model Building

• Operationalize

• Communicate Results

What is the purpose of model selection in data science?

• To choose the best programming language for data analysis

• To select the most appropriate data visualization tools

• To choose an analytical technique or a list of techniques based on the project's end goal

• To decide which type of data to collect for a project

What does the term "model" refer to in the context of data science?

• A representation of a real-world process for the purpose of simulation

• A physical prototype used in engineering

• A fashion icon that sets trends

• A mathematical equation used to calculate finances

What is depicted in the "Food Chains" diagram?

• A process flowchart for a manufacturing system

• A network diagram for computer connections

• An ecological food chain showing the flow of energy between organisms

• A family tree of a particular species

What is a model in the context of data science?

• A person who showcases clothing or other products

• A machine learning algorithm without any data

• An abstraction from reality to understand and emulate real-world behavior with rules and conditions

• A specific type of statistical test

What does modeling involve in data science?

• Creating visual representations of data only

• Observing events in a real-world situation and constructing models to emulate this behavior

• Copying data from one source to another

• Making predictions without any underlying data

What is the purpose of the 'Market Basket Example' in the context of model selection?

• To illustrate the concept of equilibrium in supply and demand

• To demonstrate how to physically arrange products in a store

• To provide an example of how data science can analyze purchasing patterns and product placement

• To show the design of a shopping cart