Chapter 1

Question 1

Artificial Intelligence

Answer 1

Artificial intelligence (AI) is a branch of computer science that is designed to mimic human-like intelligence for certain tasks, such as discovering patterns in data, recognizing objects from an image, understanding the meaning of text, and processing voice commands.

Question 2

Binary

Answer 2

Binary categorical data can have only two values—for example, yes or no. This can be represented in different ways such as 1 or 0 or “True” and “False.” Binary data is commonly used for classification in predictive modeling.

Question 3

Categorical Data

Answer 3

Categorical data exists when values represent a group of categories. Categorical variables can be one of three types: binary, nominal, or ordinal.

Question 4

Continuous Data

Question 5

Continuous Data

Answer 5

“Continuous data includes values with decimals: 1, 1.4, 3.75, . . .”

Question 6

Dependent (Target) Variable

Answer 6

The variable being predicted is referred to as the dependent (target) variable (Y).

Question 7

Descriptive Analytics

Answer 7

Descriptive analytics are a set of techniques used to explain or quantify the past.

Question 8

Discrete Data

Answer 8

Discrete data is measured in whole numbers (integers): 1, 2, 3, . . .

Question 9

Independent (Predictor) Variable

Answer 9

The variables used to make the prediction are called independent variables (X) (also referred to as predictors or features).

Question 10

Integer

Answer 10

An integer is a whole number.

Question 11

Interval

Answer 11

Interval data has an equal distance between data points and does not include an absolute zero.

Question 12

Machine Learning

Answer 12

Machine learning is a statistical method of learning that can be trained without human intervention to understand and identify relationships between previously established variables.

Question 13

Marketing Analytics

Answer 13

Marketing analytics uses data, statistics, mathematics, and technology to solve marketing business problems.

Question 14

Nominal

Answer 14

Nominal categorical data consist of characteristics that have no meaningful order.

Question 15

Ordinal

Answer 15

Ordinal categorical data represent meaningful values with a natural order but the intervals between scale points may be uneven.

Question 16

Predictive Analytics

Answer 16

Predictive analytics is used to build models based on the past to explain the future.

Question 17

Prescriptive Analytics

Answer 17

Prescriptive analytics identifies the best optimal course of action or decision.

Question 18

Primary Data

Answer 18

Primary data is collected for a specific purpose. For example, companies conduct primary research with surveys, focus groups, interviews, observations, and experiments to address problems or answer distinct questions.

Question 19

Ratio

Answer 19

Ratio values can have an absolute zero point and can be discussed in terms of multiples when comparing one point to another.

Question 20

Secondary Data

Answer 20

Secondary data relies on existing data that has been collected for another purpose.

Question 21

SMART Principles

Answer 21

SMART principles are used as a goal-setting technique. The acronym stands for specific, measurable, attainable, relevant, and timely.

Question 22

Structured Data

Answer 22

Structured data is made up of records that are organized in rows and columns. This type of data can be stored in a database or spreadsheet format.

Question 23

Supervised Learning

Answer 23

In supervised learning, the target variable of interest is known and is available in a historical dataset.

Question 24

Testing Dataset

Answer 24

A testing dataset is used to evaluate the final selection algorithm on a dataset unique from the training and validation datasets.

Question 25

Training Dataset

Answer 25

The training dataset is the data used to build the algorithm and “learn” the relationship between the predictors and the target variable.

Question 26

Unstructured Data

Answer 26

Unstructured data does not have a predefined structure and does not fit well into a table format (within rows and columns).

Question 27

Unsupervised Learning

Answer 27

Unsupervised learning has no previously defined target variable. The goal of unsupervised learning is to model the underlying structure and distribution in the data to discover and confirm patterns in the data.

Question 28

Validation Dataset

Answer 28

The validation dataset is used to assess how well the algorithm estimates the target variable, and helps select the model that most accurately predicts the target value of interest.

Question 29

Variables

Answer 29

Variables are characteristics or features that pertain to a person, place, or object. Marketing analysts explore relationships between variables to improve decision making.