WGU D204 Data Analytics Exam 2023
Questions and Answers (Verified
Answers)
Lack of Organization and Lack of Sponsor Support - ANSWER-What are some of the
main reasons that projects fail?
Hacking Skills, Math and Statistics, and Domain Expertise - ANSWER-What is the Venn
Diagram for Data Science?
Discovery - ANSWER-Define Goals, Ask Questions, Develop a Problem Statement,
Organize Resources
Data Acquisition - ANSWER-Gather Data through API's, Open Data, Passive Data, and
internal/external sources
Data Exploration - ANSWER-Make Visualizations, Summaries, Re-categorize cases,
combine variables, look at summary statistics, develop histograms
Predictive Modeling Phase - ANSWER-Create statistical models and complete linear
regression, decision trees, deep learning, machine learning, and neural networks
Data Mining - ANSWER-Discover patterns in the data using automatic and manual
methods, extraction of patterns and knowledge
In House Data - ANSWER-Data that is already available for use
Fast, minimum restrictions, might be able to talk to the people who created data set. -
ANSWER-Pro's of using In-House Data
Poor Documentation, Data may Not Exist - ANSWER-Con's of using In-House Data
Open Data - ANSWER-Data that is free and open to use
API - ANSWER-Way of sharing data, routes data, translates data, gets it ready for use.
Algebra - ANSWER-Math that allows you to scale up and generalize data
Linear Algebra - ANSWER-Math used for matrices and vectors
Calculus - ANSWER-Math used to determine maximization and minimization
,Descriptive - ANSWER-What Happened?
Predictive - ANSWER-What will happen?
Prescriptive - ANSWER-Optimization, what is the best outcome possible?
Descriptive - ANSWER-How many products were sold last quarter?
Prescriptive - ANSWER-What do we need to do to increase sales by 20% next quarter?
Predictive - ANSWER-What are the sales expected to be next quarter?
Data Analyst - ANSWER-Responsible for obtaining and scrubbing data and displaying
the results.
Data Analyst - ANSWER-Recommends statistical methods and creates data
visualizations
Research Lead - ANSWER-Knows the business, asks interesting questions, and finds
assumptions.
Project Manager - ANSWER-Works to get the data out into the organization, clears
organization hurdles, makes results actionable, and enforces learning.
Data Discovery / Business Understanding - ANSWER-Analyst defines the major
questions of interest, understands the needs of the stakeholders, and assesses the
resource constraints of the project.
Data Acquistion - ANSWER-Data retrieved from database using SQL, web scraping,
surveys, open data, API's, etc.
Data Cleaning - ANSWER-SQL, Python, Excel, R, or other tools used to perform data
modifications and transformations. Remove Null values, sort data, and get ready for the
next steps.
Data Exploration - ANSWER-The analyst begins to understand the basic nature of the
data and the relationships within it. Measures of central tendency and variability are
examined.
Predictive Modeling - ANSWER-Creating models that enable predicting outcomes of
interest. Tools such as Python and R play an important role in automating and the
training and using of models.
Data Mining - ANSWER-Ability of computers to look for patterns in large amounts of
data. Machine learning, Python and R are important in this stage.
, Reporting and Visualization - ANSWER-Analyst tells story of the data and uses graphs
or interactive dashboards to inform others of the findings. Tableau and PowerBI allow
the novice user the ability to interact with the data and spot trends and patterns.
Planning - ANSWER-Define Goals, Organize Resources, Coordinate People, and
Schedule Project
Wrangling - ANSWER-Get Data, Clean Data, Explore Data, and Refine Data
Modeling - ANSWER-Create Model, Validate Model, Evaluate Model, and Refine Model
Applying - ANSWER-Present Model, Deploy Model, Revisit Model, Archive Assets
Discovery - ANSWER-Meet with stakeholders
Discovery - ANSWER-Organize Resources
Discovery - ANSWER-Identify Business Needs
Data Acquisition - ANSWER-Getting the data, cleaning the data
Data Exploration - ANSWER-Gaining insight to data set, verification through
visualization, and getting familiar with the data set
Data Exploration - ANSWER-An analyst applies a statistical formula to obtain the
average temperature in a city over the last 50 years.
Data Exploration - ANSWER-An analyst creates a histogram to observe the summary
statistics for a data set.
Data Mining - ANSWER-Finding Patterns and Insights
Data Mining - ANSWER-Test Hypotheses
Data Mining - ANSWER-An analyst realizes that the data set has been reduced
significantly, resulting in sample sizes that are too small.
Data Mining - ANSWER-A data analyst has identified combinations of sales
transactions that frequently occur together in data over the past 5 years.
Data Acquisition - ANSWER-An analyst creates a SQL query to pull data from a data
warehouse.
Descriptive Modeling - ANSWER-Describes the data that's present. Mean, Median,
Mode, etc.
Questions and Answers (Verified
Answers)
Lack of Organization and Lack of Sponsor Support - ANSWER-What are some of the
main reasons that projects fail?
Hacking Skills, Math and Statistics, and Domain Expertise - ANSWER-What is the Venn
Diagram for Data Science?
Discovery - ANSWER-Define Goals, Ask Questions, Develop a Problem Statement,
Organize Resources
Data Acquisition - ANSWER-Gather Data through API's, Open Data, Passive Data, and
internal/external sources
Data Exploration - ANSWER-Make Visualizations, Summaries, Re-categorize cases,
combine variables, look at summary statistics, develop histograms
Predictive Modeling Phase - ANSWER-Create statistical models and complete linear
regression, decision trees, deep learning, machine learning, and neural networks
Data Mining - ANSWER-Discover patterns in the data using automatic and manual
methods, extraction of patterns and knowledge
In House Data - ANSWER-Data that is already available for use
Fast, minimum restrictions, might be able to talk to the people who created data set. -
ANSWER-Pro's of using In-House Data
Poor Documentation, Data may Not Exist - ANSWER-Con's of using In-House Data
Open Data - ANSWER-Data that is free and open to use
API - ANSWER-Way of sharing data, routes data, translates data, gets it ready for use.
Algebra - ANSWER-Math that allows you to scale up and generalize data
Linear Algebra - ANSWER-Math used for matrices and vectors
Calculus - ANSWER-Math used to determine maximization and minimization
,Descriptive - ANSWER-What Happened?
Predictive - ANSWER-What will happen?
Prescriptive - ANSWER-Optimization, what is the best outcome possible?
Descriptive - ANSWER-How many products were sold last quarter?
Prescriptive - ANSWER-What do we need to do to increase sales by 20% next quarter?
Predictive - ANSWER-What are the sales expected to be next quarter?
Data Analyst - ANSWER-Responsible for obtaining and scrubbing data and displaying
the results.
Data Analyst - ANSWER-Recommends statistical methods and creates data
visualizations
Research Lead - ANSWER-Knows the business, asks interesting questions, and finds
assumptions.
Project Manager - ANSWER-Works to get the data out into the organization, clears
organization hurdles, makes results actionable, and enforces learning.
Data Discovery / Business Understanding - ANSWER-Analyst defines the major
questions of interest, understands the needs of the stakeholders, and assesses the
resource constraints of the project.
Data Acquistion - ANSWER-Data retrieved from database using SQL, web scraping,
surveys, open data, API's, etc.
Data Cleaning - ANSWER-SQL, Python, Excel, R, or other tools used to perform data
modifications and transformations. Remove Null values, sort data, and get ready for the
next steps.
Data Exploration - ANSWER-The analyst begins to understand the basic nature of the
data and the relationships within it. Measures of central tendency and variability are
examined.
Predictive Modeling - ANSWER-Creating models that enable predicting outcomes of
interest. Tools such as Python and R play an important role in automating and the
training and using of models.
Data Mining - ANSWER-Ability of computers to look for patterns in large amounts of
data. Machine learning, Python and R are important in this stage.
, Reporting and Visualization - ANSWER-Analyst tells story of the data and uses graphs
or interactive dashboards to inform others of the findings. Tableau and PowerBI allow
the novice user the ability to interact with the data and spot trends and patterns.
Planning - ANSWER-Define Goals, Organize Resources, Coordinate People, and
Schedule Project
Wrangling - ANSWER-Get Data, Clean Data, Explore Data, and Refine Data
Modeling - ANSWER-Create Model, Validate Model, Evaluate Model, and Refine Model
Applying - ANSWER-Present Model, Deploy Model, Revisit Model, Archive Assets
Discovery - ANSWER-Meet with stakeholders
Discovery - ANSWER-Organize Resources
Discovery - ANSWER-Identify Business Needs
Data Acquisition - ANSWER-Getting the data, cleaning the data
Data Exploration - ANSWER-Gaining insight to data set, verification through
visualization, and getting familiar with the data set
Data Exploration - ANSWER-An analyst applies a statistical formula to obtain the
average temperature in a city over the last 50 years.
Data Exploration - ANSWER-An analyst creates a histogram to observe the summary
statistics for a data set.
Data Mining - ANSWER-Finding Patterns and Insights
Data Mining - ANSWER-Test Hypotheses
Data Mining - ANSWER-An analyst realizes that the data set has been reduced
significantly, resulting in sample sizes that are too small.
Data Mining - ANSWER-A data analyst has identified combinations of sales
transactions that frequently occur together in data over the past 5 years.
Data Acquisition - ANSWER-An analyst creates a SQL query to pull data from a data
warehouse.
Descriptive Modeling - ANSWER-Describes the data that's present. Mean, Median,
Mode, etc.
