WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
Business Understanding (order) - ans1st in Data Analytics Lifecycle
Data Acquisition (order) - ans2nd in Data Analytics Lifecycle
Data Cleaning (order) - ans3rd in Data Analytics Lifecycle
Data Exploration (order) - ans4th in Data Analytics Lifecycle
Predictive Modeling (order) - ans5th in Data Analytics Lifecycle
Data Mining/Machine Learning (order) - ans6th in Data Analytics Lifecycle
Reporting and Visualization (order) - ans7th in Data Analytics Lifecycle
Business Understanding - ans- Also known as the discovery phase or planning phase
- Analyst defines the major questions of interest
- Assesses the resource constraints of the project
- Determines the needs of stakeholders
Data Acquisition - ans- Collecting Data
- Data retrieved from DB
- Use SQL to obtain from Data Warehouse
- If data is not available, web scraping and surveys are used to acquire it
Data Cleaning - ans- Referred to as Data cleansing, data wrangling, data munging, and
feature engineering
- When this phase is ignored or skipped, the results from the analysis may become irrelevant.
- There is no one common tool supporting this phase. An analyst will use SQL, Python, R, or
Excel to perform various data modifications and transformations.
- Data quality is measured in terms of uniqueness and relevance.
Data Exploration - ans- the analyst begins to understand the basic nature of data and the
relationships within it.
- This phase often relies on the use of data visualization tools and numerical summaries, such
as measures of central tendency and variability.
Predictive Modeling - ans- These tools allow an analyst to move beyond describing the data
to creating models that enable predictions of outcomes of interest.
- Tools such as Python and R play an important role in automating the training and use of
models.
Data Mining - ans- These tools became popular with the ability of computers to look for
patterns in large amounts of data. Tools such as Python and R play an important role in this
phase.
- At times you may find that "machine learning" is used as a synonym for "data mining."
However, some in the industry might refer to "machine learning" as a specialized segment of
data mining techniques that continually update (i.e., "learn") to improve its modeling over
time.
Reporting and Visualization - ans- an analyst tells the story of the data and uses graphs or
interactive dashboards to inform others of the findings from the analyses.
- Interactive dashboard tools, such as Tableau, give even the novice user the ability to interact
with the data and spot trends and patterns.
- Often, the goal of this phase is to provide actionable insights for various stakeholders.
,WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
Business Understanding Problems - ansLack of clear focus on stakeholders, timeline,
limitations and budget could potentially derail an analysis
Data Acquisition Problems - ansQuality and type of data may make access more difficult
Data Cleaning Problems - ansSome cleaning techniques could dramatically change
data/outcomes
Outliers not dealt with can cause problems with statistical models due to excessive
variability.
Data Exploration Problems - ansSkipping this step could enable faulty perceptions of the data
which hurt advanced analytics.
Predictive Modeling Problems - ans- Too many input variables (predictors) can cause
problems
- Correlation does not imply causation.
- Time series models often need sufficient time data to offer precise trending.
- Predictive model accuracy should be assessed using cross-validation.
Data Mining Problems - ansRunning on entire data is problematic; need to subset data into
training and testing datasets to build models.
Reporting and visualization Problems - ans- Due to potential large audience consumption,
mistakes can cause bad business decisions and loss of revenue
- Improper scales used in graphs could push for interpretations of the story that is inaccurate
Descriptive - ansKey focus: Observation
Main question: What happened?
Diagnostics - ansKey focus: Explained reason
Main question: Why did it happen?
Descriptive Example - ansIn a healthcare setting, an unusually high number of people are
admitted to the emergency room in a short period of time. ____ analytics tells you that this is
happening and provides real-time data with all the corresponding statistics (date of
occurrence, volume, patient details, etc.).
Diagnostic Example - ansIn the healthcare example mentioned earlier, ___ analytics would
explore the data and make correlations. For instance, it may help you determine that all of the
patients' symptoms — high fever, dry cough, and fatigue — point to the same infectious
agent. You now have an explanation for the sudden spike in volume at the ER.
Predictive Example - ansBack in our hospital example, ___ analytics may forecast a surge in
patients admitted to the ER in the next several weeks. Based on patterns in the data, the
illness is spreading at a rapid rate.
Prescriptive Example - ansBack to our hospital example: now that you know the illness is
spreading, the ___ analytics tool may suggest that you increase the number of staff on hand to
adequately treat the influx of patients.
Causation - ansthere is a real-world explanation for why this is logically happening; it implies
a cause and effect
Show Causality - ansA/B testing or experiments
,WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
A/B testing example - ansyou own a website with a red login button. You're thinking that you
should change it to blue, since it looks too ugly. To determine which color you should use,
you ask your users. You randomly sample 100 users and show 50 users the red button and 50
users the blue button. You measure the ratio of people who login to your website for each
group, and see if there's a big difference. This approach randomly assigns subjects to two
groups: an A and B group.
Planning - ans1. Define the goals
2. Organize resources
3. Coordinate people
4. Schedule project
Wrangling - ans5. Get data
6. Clean data
7. Explore data
8. Refine data
Modeling - ans9. Create model
10. Validate model
11. Evaluate model
12. Refine model
Applying - ans13. Present model
14. Deploy model
15. Revisit model
16. Archive assets
Data Science Process - ans1. Find a question
2. Collect the Data
3. Prepare the Data
4. Create a model
5. Evaluate the model
6. Deploy the model
Stakeholders - ansan individual or group that has an interest in any decision or activity of an
organization. For an analyst, this could simply be the manager, or even higher executives.
Stakeholder register - ansproject document that has information about the project
stakeholders. It identifies the people, groups, and organizations that have any interest in the
work and the outcome
project sponsor - ansresponsible for identifying a project topic/scope that is well aligned with
the strengths of the data analytics project and for providing access to necessary data and key
support staff for continued project progress monitoring and timely feedback
Data analytics program managers - ans- provide direction for a team of data analysts. They
also build that team, making hiring decisions and deciding where each analyst's skills will
prove most productive for the organization. They oversee the work of an analytics
department, ensuring its functionality.
- their main goal is in collaborating with the team and make sure there is forward momentum
, WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
- solidify the day-to-day activities needed for successful completion of the project
iron triangle - ansthe ability of the project manager to deliver in time, cost, and quality
Data privacy - ansis responsibly collecting, using and storing data about people, in line with
the expectations of those people, your customers, regulations and laws
Data ethics - ansdoing the right thing with data, considering the human impact from all sides,
and making decisions based on your brand values
What regulators must review - ans- What harms are they trying to protect people from?
- What rights do they want to guarantee?
- What problems are they trying to solve?
- What are the privacy outcomes they hope to achieve for their citizens?
American Statistical Association Ethical Guidelines 1 - ansThe ethical statistician uses
methodology and data that are relevant and appropriate; without favoritism or prejudice; and
in a manner intended to produce valid, interpretable, and reproducible results. The ethical
statistician does not knowingly accept work for which he/she is not sufficiently qualified, is
honest with the client about any limitation of expertise, and consults other statisticians when
necessary or in doubt. It is essential that statisticians treat others with respect.
American Statistical Association Ethical Guidelines 2 - ansThe ethical statistician is candid
about any known or suspected limitations, defects, or biases in the data that may affect the
integrity or reliability of the statistical analysis. Objective and valid interpretation of the
results requires that the underlying analysis recognizes and acknowledges the degree of
reliability and integrity of the data.
American Statistical Association Ethical Guidelines 3 - ansThe ethical statistician supports
valid inferences, transparency, and good science in general, keeping the interests of the
public, funder, client, or customer in mind (as well as professional colleagues, patients, the
public, and the scientific community).
American Statistical Association Ethical Guidelines 4 - ansThe ethical statistician protects
and respects the rights and interests of human and animal subjects at all stages of their
involvement in a project. This includes respondents to the census or to surveys, those whose
data are contained in administrative records, and subjects of physically or psychologically
invasive research.
American Statistical Association Ethical Guidelines 5 - ansScience and statistical practice are
often conducted in teams made up of professionals with different professional standards. The
statistician must know how to work ethically in this environment.
American Statistical Association Ethical Guidelines 6 - ansThe practice of statistics requires
consideration of the entire range of possible explanations for observed phenomena, and
distinct observers drawing on their own unique sets of experiences can arrive at different and
potentially diverging judgments about the plausibility of different explanations. Even in
adversarial settings, discourse tends to be most successful when statisticians treat one another
with mutual respect and focus on scientific principles, methodology, and the substance of
data interpretations.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
Business Understanding (order) - ans1st in Data Analytics Lifecycle
Data Acquisition (order) - ans2nd in Data Analytics Lifecycle
Data Cleaning (order) - ans3rd in Data Analytics Lifecycle
Data Exploration (order) - ans4th in Data Analytics Lifecycle
Predictive Modeling (order) - ans5th in Data Analytics Lifecycle
Data Mining/Machine Learning (order) - ans6th in Data Analytics Lifecycle
Reporting and Visualization (order) - ans7th in Data Analytics Lifecycle
Business Understanding - ans- Also known as the discovery phase or planning phase
- Analyst defines the major questions of interest
- Assesses the resource constraints of the project
- Determines the needs of stakeholders
Data Acquisition - ans- Collecting Data
- Data retrieved from DB
- Use SQL to obtain from Data Warehouse
- If data is not available, web scraping and surveys are used to acquire it
Data Cleaning - ans- Referred to as Data cleansing, data wrangling, data munging, and
feature engineering
- When this phase is ignored or skipped, the results from the analysis may become irrelevant.
- There is no one common tool supporting this phase. An analyst will use SQL, Python, R, or
Excel to perform various data modifications and transformations.
- Data quality is measured in terms of uniqueness and relevance.
Data Exploration - ans- the analyst begins to understand the basic nature of data and the
relationships within it.
- This phase often relies on the use of data visualization tools and numerical summaries, such
as measures of central tendency and variability.
Predictive Modeling - ans- These tools allow an analyst to move beyond describing the data
to creating models that enable predictions of outcomes of interest.
- Tools such as Python and R play an important role in automating the training and use of
models.
Data Mining - ans- These tools became popular with the ability of computers to look for
patterns in large amounts of data. Tools such as Python and R play an important role in this
phase.
- At times you may find that "machine learning" is used as a synonym for "data mining."
However, some in the industry might refer to "machine learning" as a specialized segment of
data mining techniques that continually update (i.e., "learn") to improve its modeling over
time.
Reporting and Visualization - ans- an analyst tells the story of the data and uses graphs or
interactive dashboards to inform others of the findings from the analyses.
- Interactive dashboard tools, such as Tableau, give even the novice user the ability to interact
with the data and spot trends and patterns.
- Often, the goal of this phase is to provide actionable insights for various stakeholders.
,WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
Business Understanding Problems - ansLack of clear focus on stakeholders, timeline,
limitations and budget could potentially derail an analysis
Data Acquisition Problems - ansQuality and type of data may make access more difficult
Data Cleaning Problems - ansSome cleaning techniques could dramatically change
data/outcomes
Outliers not dealt with can cause problems with statistical models due to excessive
variability.
Data Exploration Problems - ansSkipping this step could enable faulty perceptions of the data
which hurt advanced analytics.
Predictive Modeling Problems - ans- Too many input variables (predictors) can cause
problems
- Correlation does not imply causation.
- Time series models often need sufficient time data to offer precise trending.
- Predictive model accuracy should be assessed using cross-validation.
Data Mining Problems - ansRunning on entire data is problematic; need to subset data into
training and testing datasets to build models.
Reporting and visualization Problems - ans- Due to potential large audience consumption,
mistakes can cause bad business decisions and loss of revenue
- Improper scales used in graphs could push for interpretations of the story that is inaccurate
Descriptive - ansKey focus: Observation
Main question: What happened?
Diagnostics - ansKey focus: Explained reason
Main question: Why did it happen?
Descriptive Example - ansIn a healthcare setting, an unusually high number of people are
admitted to the emergency room in a short period of time. ____ analytics tells you that this is
happening and provides real-time data with all the corresponding statistics (date of
occurrence, volume, patient details, etc.).
Diagnostic Example - ansIn the healthcare example mentioned earlier, ___ analytics would
explore the data and make correlations. For instance, it may help you determine that all of the
patients' symptoms — high fever, dry cough, and fatigue — point to the same infectious
agent. You now have an explanation for the sudden spike in volume at the ER.
Predictive Example - ansBack in our hospital example, ___ analytics may forecast a surge in
patients admitted to the ER in the next several weeks. Based on patterns in the data, the
illness is spreading at a rapid rate.
Prescriptive Example - ansBack to our hospital example: now that you know the illness is
spreading, the ___ analytics tool may suggest that you increase the number of staff on hand to
adequately treat the influx of patients.
Causation - ansthere is a real-world explanation for why this is logically happening; it implies
a cause and effect
Show Causality - ansA/B testing or experiments
,WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
A/B testing example - ansyou own a website with a red login button. You're thinking that you
should change it to blue, since it looks too ugly. To determine which color you should use,
you ask your users. You randomly sample 100 users and show 50 users the red button and 50
users the blue button. You measure the ratio of people who login to your website for each
group, and see if there's a big difference. This approach randomly assigns subjects to two
groups: an A and B group.
Planning - ans1. Define the goals
2. Organize resources
3. Coordinate people
4. Schedule project
Wrangling - ans5. Get data
6. Clean data
7. Explore data
8. Refine data
Modeling - ans9. Create model
10. Validate model
11. Evaluate model
12. Refine model
Applying - ans13. Present model
14. Deploy model
15. Revisit model
16. Archive assets
Data Science Process - ans1. Find a question
2. Collect the Data
3. Prepare the Data
4. Create a model
5. Evaluate the model
6. Deploy the model
Stakeholders - ansan individual or group that has an interest in any decision or activity of an
organization. For an analyst, this could simply be the manager, or even higher executives.
Stakeholder register - ansproject document that has information about the project
stakeholders. It identifies the people, groups, and organizations that have any interest in the
work and the outcome
project sponsor - ansresponsible for identifying a project topic/scope that is well aligned with
the strengths of the data analytics project and for providing access to necessary data and key
support staff for continued project progress monitoring and timely feedback
Data analytics program managers - ans- provide direction for a team of data analysts. They
also build that team, making hiring decisions and deciding where each analyst's skills will
prove most productive for the organization. They oversee the work of an analytics
department, ensuring its functionality.
- their main goal is in collaborating with the team and make sure there is forward momentum
, WGU D204 Masters of Data Analytics Journey.
A Comprehensive Exam Study Guide
Latest Updated 2025/2026.
100% Certified and Verified by Expert.
- solidify the day-to-day activities needed for successful completion of the project
iron triangle - ansthe ability of the project manager to deliver in time, cost, and quality
Data privacy - ansis responsibly collecting, using and storing data about people, in line with
the expectations of those people, your customers, regulations and laws
Data ethics - ansdoing the right thing with data, considering the human impact from all sides,
and making decisions based on your brand values
What regulators must review - ans- What harms are they trying to protect people from?
- What rights do they want to guarantee?
- What problems are they trying to solve?
- What are the privacy outcomes they hope to achieve for their citizens?
American Statistical Association Ethical Guidelines 1 - ansThe ethical statistician uses
methodology and data that are relevant and appropriate; without favoritism or prejudice; and
in a manner intended to produce valid, interpretable, and reproducible results. The ethical
statistician does not knowingly accept work for which he/she is not sufficiently qualified, is
honest with the client about any limitation of expertise, and consults other statisticians when
necessary or in doubt. It is essential that statisticians treat others with respect.
American Statistical Association Ethical Guidelines 2 - ansThe ethical statistician is candid
about any known or suspected limitations, defects, or biases in the data that may affect the
integrity or reliability of the statistical analysis. Objective and valid interpretation of the
results requires that the underlying analysis recognizes and acknowledges the degree of
reliability and integrity of the data.
American Statistical Association Ethical Guidelines 3 - ansThe ethical statistician supports
valid inferences, transparency, and good science in general, keeping the interests of the
public, funder, client, or customer in mind (as well as professional colleagues, patients, the
public, and the scientific community).
American Statistical Association Ethical Guidelines 4 - ansThe ethical statistician protects
and respects the rights and interests of human and animal subjects at all stages of their
involvement in a project. This includes respondents to the census or to surveys, those whose
data are contained in administrative records, and subjects of physically or psychologically
invasive research.
American Statistical Association Ethical Guidelines 5 - ansScience and statistical practice are
often conducted in teams made up of professionals with different professional standards. The
statistician must know how to work ethically in this environment.
American Statistical Association Ethical Guidelines 6 - ansThe practice of statistics requires
consideration of the entire range of possible explanations for observed phenomena, and
distinct observers drawing on their own unique sets of experiences can arrive at different and
potentially diverging judgments about the plausibility of different explanations. Even in
adversarial settings, discourse tends to be most successful when statisticians treat one another
with mutual respect and focus on scientific principles, methodology, and the substance of
data interpretations.
