What is the first step Emma takes when working on a data science project?

• Data modeling

• Exploratory Data Analysis

• Data acquisition

• Understanding the business problem

Why is intellectual curiosity important for a data scientist like Emma?

• It helps gather more data.

• It ensures the use of the best algorithms.

• It drives deeper exploration and questioning.

• It makes client meetings shorter.

What tools does Emma use for complex data transformations?

• Python and R

• Talend and Informatica

• Excel and PowerBI

• Tableau and QlikView

What happens if Emma skips the Exploratory Data Analysis step?

• The model may become more accurate.

• The model could use the wrong variables and produce inaccurate results.

• The data cleaning process will be easier.

• The model selection becomes simpler.

Which of the following is NOT mentioned as a data visualization tool Emma uses?

• PowerBI

• QlikView

• Tableau

• Excel

Which of the following scenarios would require Emma to conduct data cleaning?

• The data already contains well-defined features.

• There are missing or duplicated values in the dataset.

• The business problem is not clearly defined.

• The client is familiar with all technical terms.

What is the key reason for Emma using multiple machine learning techniques like KNN and Naive Bayes?

• To make sure she uses the easiest technique

• To find the model that best fits the business requirement

• To avoid cleaning the data

• To gather more data from multiple sources

If a data scientist like Emma wants to communicate findings effectively to a non-technical audience, what is the best approach?

• Use complex algorithms to impress the audience

• Avoid using any visualizations and provide raw data

• Use simple and effective storytelling along with visual tools like Tableau or PowerBI

• Focus solely on technical jargon

How does Emma ensure the selected model is reliable before deployment?

• She trains and tests it on the same dataset.

• She deploys it directly in the production environment.

• She tests it in a pre-production environment to ensure stability.

• She skips the testing phase to save time.

What could be a downside if Emma does not clarify the business problem at the start of the project?

• She may collect data that is irrelevant to the problem.

• She will spend less time on data transformation.

• She will have a quicker meeting with stakeholders.

• Her model will automatically become more accurate.