Module 1

Question 1

Descriptive Analytics

Answer 1

Past Data; Inform/Explantory

Descriptive Analytics deals with analyzing past data.

Question 2

Predictive Analytics

Answer 2

Past Data to Predict Future

Predictive Analytics uses past data to predict future outcomes.

Question 3

Prescriptive Analytics

Answer 3

Past Data to Predict future + Optimizing (changing)

Prescriptive Analytics uses past data to predict future outcomes and optimize by making changes.

Question 4

Systematic Error

Answer 4

Error that repeats itself; Will not fix itself, you have to fix it.

Systematic Error requires intervention to be resolved.

Question 5

Random Error

Answer 5

Unpredictable Error; Will fix itself, goes away with you fixing it.

Random Error disappears once the issue is addressed.

Question 6

Outlier

Answer 6

Is a number in the dataset that are different from others.

An outlier is a data point that significantly differs from the rest.

Question 7

Out of Range Error

Answer 7

When you made a mistake.

Out of Range Error occurs when an error is made in data input.

Question 8

Omission

Answer 8

Missing important information, causing inaccurate results.

Omission refers to leaving out crucial data that impacts accuracy.

Question 9

Reliable Data

Answer 9

Consistent & Repeatable

Reliable Data is data that is consistent and can be replicated.

Question 10

Valid Data

Answer 10

Measures what is intended to be measured

Valid Data accurately measures the intended aspect.

Question 11

Measurement Bias

Answer 11

Non-Representative Sample; Non-Random Sample. Sample has to be 30 or more.

Measurement Bias can be reduced by ensuring a representative sample of at least 30.

Question 12

Information Bias

Answer 12

Ignoring the purpose of the information collected; non-truthful answers.

Information Bias occurs when the purpose of data collection is disregarded.

Question 13

Big Data

Answer 13

Both structured & unstructured data that is too large to process using traditional database & software.

Big Data refers to large volumes of data that require specialized tools for processing.

Question 14

Data Mining

Answer 14

Process of discovering patterns in large data sets.

Data Mining involves extracting patterns from extensive datasets.

Question 15

Structured Data

Answer 15

Organized, easily searchable (e.g., rows, colums).

Structured Data is organized and searchable, typically found in databases and spreadsheets.

Question 16

Unstructured Data

Answer 16

Unorganized, harder to process (e.g., emails, videos, social media posts).

Unstructured Data lacks organization and is challenging to process, such as emails, videos, and social media posts.

Question 17

Nominal

Answer 17

Names or Labels w/ no order (NO NUMBER)

Nominal level of measurement consists of names or labels with no inherent order.

Question 18

Ordinal

Answer 18

Order Matters; categories in order (NO NUMBERS)

Ordinal level of measurement involves categories with a specific order.

Question 19

Interval

Answer 19

Orders with equal gaps, no real zero (e.g. temperature) (NUMBER)

Interval level of measurement has equal intervals between values but no true zero point.

Question 20

Ratio

Answer 20

Like Interval but with a Real Zero (height, weight) (NUMBER)

Ratio level of measurement is similar to interval but includes a true zero point.

Question 21

Optimization

Answer 21

Best Choice

Question 22

Decision Analysis

Answer 22

Helps makes the best choice by weighing risks and rewards

Question 23

Simulation

Answer 23

Helps you see what might happen

Question 24

Davenport-Kim 3 Stage Model:

Answer 24

1. Frame the Problem
2. Solving the Problem
3. Communicating Results

Question 25

Davenport-Kim 3 Stage Model:
Framing the Problem

Answer 25

-Problem Recognition
-Review of Previous Findings

Question 26

Davenport-Kim 3 Stage Model:
Solving the Problem

Answer 26

-Choose the Model
-Collect the Data
-Analyze the Data

Question 27

Davenport-Kim 3 Stage Model:
Communicating Results

Answer 27

-Communicate Results
-Act on Results

Question 28

Qualitative Research

Answer 28

Explores ideas & experiences through non-numerical data (i.e., textual, visual, or oral)

Question 29

Quantitative Research

Answer 29

Quantifies the problem using numerical data; Measurements & Analytics.

Question 30

Experimental Study

Answer 30

Researchers manipulate variables to observe effects, allowing for causation inference.

Question 31

3 Elements of Experimental Study

Answer 31

1. Experimental Units - Participants/Objects
2. Treatments - procedure applied to participants/object
3. Responses - the effect of the Experimental treatment

Question 32

Observational Study

Answer 32

Researchers observe without interference, identifying correlations but not causation.

Question 33

Correlation

Answer 33

A relationship where two variables move together, but one doesn’t cause the other.

Question 34

Association vs. Causation

Answer 34

Association is a link of 2 variables; Causation is a direct effect of 1 variable.