Section 2

Question 1

—- focus on the benefits and implications of findings, while — focus on the business impact, risks, and return on investment

Answer 1

Business users, project sponsors

Question 2

A situation in which the inputs to the model are outside the range it was trained on, potentially causing inaccurate or invalid outputs

Answer 2

Out-of-bounds operation

Question 3

The system where the model is deployed and integrated with existing business processes as opposed to a sandbox or testing environment

Answer 3

Production environment

Question 4

A small-scale deployment of the model in a live setting, allowing the data science team to manage risk, evaluate performance, and adjustments before a full-scale deployment

Answer 4

Pilot project

Question 5

What is data? What is information?

Answer 5

Data is the raw material used by analysts, while information refers to processed or organized data

Question 6

What order does the data analytics lifecycle follow?

Answer 6

Discovery phase, Data preparation phase, Model planning phase, Model execution phase, Communicate results phase, Operationalize phase

Question 7

The data analytics team familiarizes themselves with the business domain, examines relevant historical data, and assesses available resources.It also involves framing the business problem as an analytics challenge and formulating initial hypotheses to test and explore the data

Answer 7

Discovery phase

Question 8

Requires the establishment of an analytic sandbox where the team can work with data and perform analytics throughout the project

Answer 8

Data preparation

Question 9

The team determines the methods, techniques, and workflow to be used during the subsequent model building phase

Answer 9

Model planning

Question 10

The team develops datasets for testing, training, and production purposes, builds and executes models based on the planning phase and evaluates the need for more robust tools or environments for executing models and workflows

Answer 10

Model execution

Question 11

Involves determining the project’s success or failure based on the criteria developed in the discovery phase. The team identifies key findings, quantifies the business value, and develops a narrative to summarize and communicate the results to stakeholders

Answer 11

Communicate results

Question 12

The team delivers, reports, briefings, code, and technical documents. A pilot project may be implemented to test the models in a production environment, ensuring that the results are framed effectively and demonstrate clear value to stakeholders

Answer 12

Operationalization

Question 13

Refers to the vast amount of information collected, stored and analyzed by businesses and organizations; its unique aspects can differ between organizations and include up to 7 characteristics; however, for this course, we will focus on the main 4 variety, velocity, veracity, and volume

Answer 13

Big data

Question 14

The diverse types of data,including structured, semi-structured, and unstructured formats; big data comes from numerous sources

Answer 14

Variety

Question 15

The speed at. which data is produced, collected and processed; in the context of big data, velocity refers to the need for quick analysis and decision-making based on the data gathered

Answer 15

Velocity

Question 16

The accuracy, reliability and quality of the data collected and analyzed; ensuring data — is essential for gaining valuable insights and making informed decisions

Answer 16

Veracity

Question 17

The sheer amount of data generaetd and handled by businesses; big data involves dealing with enormous quantities of data ranging from terabytes to petabytes and beyond, which can be challenging in terms of storage and processing

Answer 17

Volume

Question 18

By the end of this phase, the project team should have a clear understanding of the business problem nd the data available and should be ready to move forward to the analysis phase

Answer 18

Discovery phase

Question 19

Items necessary for a successful project; can include items such as technology, tools, systems, data, and people

Answer 19

Resources

Question 20

The process of stating the data analytics problem to be solved

Answer 20

Framing

Question 21

Involves data mining, which refers to the process of discovering hidden patterns, trends and insights in large datasets, that can then be used by an organization to make informed decisions

Answer 21

Data preparation phase

Question 22

The extract, load, transfomr process is a key aspect of —-, which combines data transformation flexibility with data preservation

Answer 22

Data preparation

Question 23

Programming language and software framework for statistical analysis and graphics available under the GNU General public license

Answer 23

R

Question 24

Emphasizes identifying appropriate models for clustering, classification or uncovering relationships that correspond with the hypotheses establsihed in the discovery phase

Answer 24

Model planning phase

Question 25

Is a technique used in data analytics to group similar objects or data points together based on their characteristics or attributes

Answer 25

Clustering

Question 26

This phase includes evauluating the structure of datasets, ensuring analytical techniques align with business objectives, deciding on a single model or a series of techniques, and examining existing approaches to similar problems

Answer 26

Model planning phase

Question 27

What can contribute to efficient model planning>

Answer 27

R, SQL Analysis services, Python, Apache Spark, RapidMiner, and KNIME

Question 28

Data organized in a specific format or schema, making it easier to analyze

Answer 28

Structured data

Question 29

Data that lacks a specific format or structure often requiring additional processing before analysis

Answer 29

Unstructured data

Question 30

Is dedicated to developing datasets for various purposes, such as training, testing, and production.Initially, training data is created for model development, while hold-out data is set aside for model evaluation

Answer 30

Model execution phase

Question 31

A separate dataset, also called hold-out data, used to evaluate the models performance and accuracy on unseen data

Answer 31

Test data

Question 32

Includes developing an fitting an analytical model on the training data

Answer 32

Model building phase

Question 33

The process of evaluating the technical merits of a model, such as accuracy, comprehensibility, and confidence in predictions

Answer 33

Model assessment

Question 34

The percentage of records classified correctly or incorrectly, used to measure the accuracy of a model

Answer 34

Error rate

Question 35

Measure that indicates the change in concentration of a particular class when the model is used to select a group from the general population

Answer 35

Lift

Question 36

A performance measurement for binary response models, comparing the true positive rate with the false positive rate

Answer 36

ROC Charts

Question 37

Analysts compare outcomes to success and failure criteria, articulate findings for stakeholders and assess the significance of their results

Answer 37

Communicate results phase

Question 38

Individuals or groups interested in the project and its outcomes

Answer 38

Stakeholders

Question 39

Measures whether the observed results are likely to have occurred by chance or if they indicate a genuine relationship between variables

Answer 39

Statistical significance

Question 40

This phase marks the first time most analytics teams deploy new analytical methods or models to a production environment

Answer 40

Operationalize

Question 41

This approach allows the team to evaluate the model's performance and make necessary adjustments in a live setting before implementing it across the enterprise

Answer 41

Operationalize phase

Question 42

A small-scale deployment of the model in a live setting, allowing the data science team to manage risk, evaluate performance, and make adjustments before a full-scale deployment

Answer 42

Pilot project

Question 43

The system where the model is deployed and integrated with existing business processes as opposed to a sandbox or testing environment

Answer 43

Production environement

Question 44

A situation in which the inputs to the model are outside the range it was trained on, potentially causing inaccurate or invalid outputs

Answer 44

Out-of-bounds-operations