WGU D204 The Data Analytics Journey Questions with
verified answers
1) The Data Analytics Life Cycle Ans✓✓✓ Trace the phases of the data analytics
life cycle.
Summarize the discovery phase of the data analytics life cycle.
Summarize the data acquisition phase of the data analytics life cycle.
Summarize the data exploration phase of the data analytics life cycle.
Summarize the predictive modeling phase of the data analytics life cycle.
Summarize the data mining phase of the data analytics life cycle.
Contextualize each phase within the data analytics life cycle.
2) Organizational Needs Ans✓✓✓ Identify the decisions necessary to initiate a
data analytics project.
Delineate clear outcomes and undefined outcomes of potential data analytics
projects.
Define clear metrics, deliverables, and value that a data analytics project will
provide to an organization.
Compare data types and sources that organizations often have available.
Define specific research questions and hypotheses based on organizational needs
and available data.
Match available data types to the possible analytical approaches they enable.
3) Ethical and Legal Issues Ans✓✓✓ Summarize the legal frameworks governing
data.
Examine fairness issues raised by data analytics projects.
Examine justice issues raised by data analytics projects.
,Examine issues of human and computer agency.
4) Teamwork and Collaboration Ans✓✓✓ Exemplify effective interpersonal
communication.
Describe co-creation approaches and tools.
5) Data Analytics Tools and Techniques Ans✓✓✓ Describe sources of data.
Describe data analytics applications.
Compare scripting and programming languages used in data analytics.
Describe machine learning as a service (MLaaS).
Summarize mathematical tools used in data science.
Describe descriptive methods.
Describe predictive and prescriptive models.
Describe trend analysis methods.
Describe cluster analysis methods.
Describe classification methods.
Describe anomaly detection methods.
Describe dimensionality reduction and feature selection.
Describe tools for delivering actionable insights to human decision-makers.
Describe machine learning and artificial intelligence.
6) Data Analytics Projects Ans✓✓✓ Define the roles of analysts in the workplace.
Define the roles of potential partners and stakeholders in data analytics projects.
Summarize the challenge of balancing resources, timelines, and quality in data
analytics projects.
, Explain the critical path and its relation to project timelines.
Estimate timelines for data analytics project deliverables.
Active listening Ans✓✓✓ Seeking to understand the speaker's emotions and
intent.
Activities performed in Data Minining Ans✓✓✓ Performance of a correlation
analysis. Finding patterns in combinations of past data. Resampling may result
when there is too little data for training and testing data sets. Sample size is an
issue here.
Activities performed in Reporting and Representation Ans✓✓✓ Create the story,
provide conclusions, and provide actionable items. Effective: do not use unrelated
data.
Activities performed in the Acquisition Phase Ans✓✓✓ Collect data; clean data
(most time and effort), identify outliers, find missing values; disqualify data
sources, transform improperly formatted text. They may develop SQL queries of
data within tables.
Activities performed in the Discovery Phase Ans✓✓✓ Identifying business needs;
define goals
Activities performed in the Exploratory Phase Ans✓✓✓ Build histograms, get
acquainted with the data, visuals. Perform statistical analysis. Poor attention here
analyst will lack insight into the structure of the data. Bar charts. Create
hypothesis.
verified answers
1) The Data Analytics Life Cycle Ans✓✓✓ Trace the phases of the data analytics
life cycle.
Summarize the discovery phase of the data analytics life cycle.
Summarize the data acquisition phase of the data analytics life cycle.
Summarize the data exploration phase of the data analytics life cycle.
Summarize the predictive modeling phase of the data analytics life cycle.
Summarize the data mining phase of the data analytics life cycle.
Contextualize each phase within the data analytics life cycle.
2) Organizational Needs Ans✓✓✓ Identify the decisions necessary to initiate a
data analytics project.
Delineate clear outcomes and undefined outcomes of potential data analytics
projects.
Define clear metrics, deliverables, and value that a data analytics project will
provide to an organization.
Compare data types and sources that organizations often have available.
Define specific research questions and hypotheses based on organizational needs
and available data.
Match available data types to the possible analytical approaches they enable.
3) Ethical and Legal Issues Ans✓✓✓ Summarize the legal frameworks governing
data.
Examine fairness issues raised by data analytics projects.
Examine justice issues raised by data analytics projects.
,Examine issues of human and computer agency.
4) Teamwork and Collaboration Ans✓✓✓ Exemplify effective interpersonal
communication.
Describe co-creation approaches and tools.
5) Data Analytics Tools and Techniques Ans✓✓✓ Describe sources of data.
Describe data analytics applications.
Compare scripting and programming languages used in data analytics.
Describe machine learning as a service (MLaaS).
Summarize mathematical tools used in data science.
Describe descriptive methods.
Describe predictive and prescriptive models.
Describe trend analysis methods.
Describe cluster analysis methods.
Describe classification methods.
Describe anomaly detection methods.
Describe dimensionality reduction and feature selection.
Describe tools for delivering actionable insights to human decision-makers.
Describe machine learning and artificial intelligence.
6) Data Analytics Projects Ans✓✓✓ Define the roles of analysts in the workplace.
Define the roles of potential partners and stakeholders in data analytics projects.
Summarize the challenge of balancing resources, timelines, and quality in data
analytics projects.
, Explain the critical path and its relation to project timelines.
Estimate timelines for data analytics project deliverables.
Active listening Ans✓✓✓ Seeking to understand the speaker's emotions and
intent.
Activities performed in Data Minining Ans✓✓✓ Performance of a correlation
analysis. Finding patterns in combinations of past data. Resampling may result
when there is too little data for training and testing data sets. Sample size is an
issue here.
Activities performed in Reporting and Representation Ans✓✓✓ Create the story,
provide conclusions, and provide actionable items. Effective: do not use unrelated
data.
Activities performed in the Acquisition Phase Ans✓✓✓ Collect data; clean data
(most time and effort), identify outliers, find missing values; disqualify data
sources, transform improperly formatted text. They may develop SQL queries of
data within tables.
Activities performed in the Discovery Phase Ans✓✓✓ Identifying business needs;
define goals
Activities performed in the Exploratory Phase Ans✓✓✓ Build histograms, get
acquainted with the data, visuals. Perform statistical analysis. Poor attention here
analyst will lack insight into the structure of the data. Bar charts. Create
hypothesis.
