Match the purpose to the discipline or concept. Used only once.

Data engineering

A.	provide data without any labels for the algorithm to discover patterns in

Data analytics

B.	build models to answer business questions and offer new automated services

Data science

C.	create databases, store data, maintain the databases.

unsupervised machine learning

E.	provide data with the correct answers (labels) for an algorithm to create a model from

10 points

QUESTION 2

What tools does a data engineer use? Select all that apply, according to the lecture.

		Databases like MS SQL Server or PostgreSQL
		BI dashboards
		SQL
		MS Excel

10 points

QUESTION 3

This image displays key measures of productivity for a supervisor. What is this image an example of? The details in the image do not matter to the question.

		machine learning
		BI dashboard
		database
		SQL

2 points

QUESTION 4

Microsoft's PowerBI and Salesforce's Tableau are examples of what type of software tool?

		supervised machine learning
		dashboard
		database
		unsupervised machine learning

6 points

QUESTION 5

Which discipline creates BI dashboards, according to the lecture? Select only one answer.

		data engineering
		data science
		programmer
		data analytics

6 points

QUESTION 6

What's the consequence of providing too little data to a machine learnning algorithm?

		the model will have higher accuracy
		the model will be flawed or biased
		the model can bypass testing and go straight into production
		inconseqeuential

6 points

QUESTION 7

In supervised learning, what is the difference between the training dataset and testing dataset? One statement is true, three are false. Identify the correct/true answer.

		The training dataset is smaller than the testing dataset.
		The training dataset is used to create the model and the testing dataset evaluates the accuracy of the model.
		Supervised learning does not use training and testing dataset.
		The training dataset includes the correct answers (labels) but the testing dataset does not include the labels.