WGU D204 Exam 2023-2024 Questions
and Answers (Graded A)
Business Understanding/ Discovery phase - ANSWER-An analyst defines the major
questions of interest that need to be answered, determines the needs of the
stakeholders, and assesses the resource constraints of the project. Define project
outcomes.
Data Acquisition - ANSWER-Collecting data phase. Data is collected and stored, for
easy retrieval from a database, perhaps a component of a data warehouse, by using a
language like SQL. Web scraping and surveys to acquire data.
Data Cleaning - ANSWER-Also known as data cleansing, data wrangling, data
munging, and feature engineering. Analyst will use SQL, Python, R, or Excel to perform
data modifications and transformations
Data Exploration - ANSWER-Analyst begins to understand the basic nature of data, the
relationships within it (btw data variables), the structure of the dataset, the presence of
outliers, and the distribution of data values. This phase uses data visualization tools and
numerical summaries such as measures of central tendency and variability.
Predictive Modeling - ANSWER-Allows the analyst to move beyond describing the data
to creating models that enable predictions of outcomes of interest. Python and R are
used in automating the training and use of models.
Data Mining - ANSWER-Looks for patterns in large sets of data. Tools are Python and
R. Also called Machine learning. A specialized segment of data mining techniques that
continually update to improve modeling over time.
Data Exploration - ANSWER-An initial step to uncover initial patterns and using both
manual and automated methods.
Data Mining - ANSWER-An in- depth step to discover patters using automated methods
like machine learning.
Data Reporting - ANSWER-Analyst tells the story of the data and uses graphs or
interactive dashboards to inform others of the findings from the analyses. Tools such as
Tableau is used to spot trends and patterns. Goal is to give actionable insight to
stakeholders.
Descriptive Analytics - ANSWER-What happened? - Observation/Describe event. It is
the interpretation of historical data to better explain market developments.
, Diagnostic Analytics - ANSWER-Why did it happen? - Explains the reason for the event.
It enables the extraction of value from data by posing the right questions and conducting
in-depth investigations into the problems.
Predictive Analytics - ANSWER-What will happen? - Correlation. Predicts what will
happen in the future. It uses data, statistical algorithms, and machine learning
techniques to determine the JS of potential outcomes. The aim is to have the best
assessment of what will happen in the future, rather than simply understanding what
has happened.
Linear Regression - ANSWER-Used to predict the value of variable based on the value
of another variable. The variable to be predicted is called dependent variable and the
variable used to predict the target variable is independent variable.
Prescriptive Analytics - ANSWER-How can we make it happen? - Keywords:
Change/Action/Solution/Causality/Manipulation/Decision Making. It helps organizations
make decisions.
Predictive and Prescriptive analytics - ANSWER-What are two forward-looking tools
used by business leaders? ___________ analytics uses collected data to come up with
future outcomes, and ____________ analytics takes that data and make decisions that
cause future outcomes.
Structured - ANSWER-Which data type is numbered and labeled stored in an organized
framework with columns and rows, e.g. Sql, databases, Excel, etc.
Semi-Structured - ANSWER-Which data type is loosely organized in categories using
tags. e.g. Emails, CSV, XML, JSON doc., etc.
Unstructured - ANSWER-Which data type is text heavy, information not organized in
clearly defined framework. e.g. text, videos, audios, etc.
Quantitative - ANSWER-Which data type is known as numerical, parametric, or interval
data.
Qualitative - ANSWER-Which data type is known as nominal or ordinal. Describes the
basic features of the data in a study.
Relational Database - ANSWER-What is a collection of data items with predefined
relationships between them e.g. collection of tables.
Data Sources - ANSWER-In house, open Data, web server, data lake, data warehouse,
self-generated are types of what?
and Answers (Graded A)
Business Understanding/ Discovery phase - ANSWER-An analyst defines the major
questions of interest that need to be answered, determines the needs of the
stakeholders, and assesses the resource constraints of the project. Define project
outcomes.
Data Acquisition - ANSWER-Collecting data phase. Data is collected and stored, for
easy retrieval from a database, perhaps a component of a data warehouse, by using a
language like SQL. Web scraping and surveys to acquire data.
Data Cleaning - ANSWER-Also known as data cleansing, data wrangling, data
munging, and feature engineering. Analyst will use SQL, Python, R, or Excel to perform
data modifications and transformations
Data Exploration - ANSWER-Analyst begins to understand the basic nature of data, the
relationships within it (btw data variables), the structure of the dataset, the presence of
outliers, and the distribution of data values. This phase uses data visualization tools and
numerical summaries such as measures of central tendency and variability.
Predictive Modeling - ANSWER-Allows the analyst to move beyond describing the data
to creating models that enable predictions of outcomes of interest. Python and R are
used in automating the training and use of models.
Data Mining - ANSWER-Looks for patterns in large sets of data. Tools are Python and
R. Also called Machine learning. A specialized segment of data mining techniques that
continually update to improve modeling over time.
Data Exploration - ANSWER-An initial step to uncover initial patterns and using both
manual and automated methods.
Data Mining - ANSWER-An in- depth step to discover patters using automated methods
like machine learning.
Data Reporting - ANSWER-Analyst tells the story of the data and uses graphs or
interactive dashboards to inform others of the findings from the analyses. Tools such as
Tableau is used to spot trends and patterns. Goal is to give actionable insight to
stakeholders.
Descriptive Analytics - ANSWER-What happened? - Observation/Describe event. It is
the interpretation of historical data to better explain market developments.
, Diagnostic Analytics - ANSWER-Why did it happen? - Explains the reason for the event.
It enables the extraction of value from data by posing the right questions and conducting
in-depth investigations into the problems.
Predictive Analytics - ANSWER-What will happen? - Correlation. Predicts what will
happen in the future. It uses data, statistical algorithms, and machine learning
techniques to determine the JS of potential outcomes. The aim is to have the best
assessment of what will happen in the future, rather than simply understanding what
has happened.
Linear Regression - ANSWER-Used to predict the value of variable based on the value
of another variable. The variable to be predicted is called dependent variable and the
variable used to predict the target variable is independent variable.
Prescriptive Analytics - ANSWER-How can we make it happen? - Keywords:
Change/Action/Solution/Causality/Manipulation/Decision Making. It helps organizations
make decisions.
Predictive and Prescriptive analytics - ANSWER-What are two forward-looking tools
used by business leaders? ___________ analytics uses collected data to come up with
future outcomes, and ____________ analytics takes that data and make decisions that
cause future outcomes.
Structured - ANSWER-Which data type is numbered and labeled stored in an organized
framework with columns and rows, e.g. Sql, databases, Excel, etc.
Semi-Structured - ANSWER-Which data type is loosely organized in categories using
tags. e.g. Emails, CSV, XML, JSON doc., etc.
Unstructured - ANSWER-Which data type is text heavy, information not organized in
clearly defined framework. e.g. text, videos, audios, etc.
Quantitative - ANSWER-Which data type is known as numerical, parametric, or interval
data.
Qualitative - ANSWER-Which data type is known as nominal or ordinal. Describes the
basic features of the data in a study.
Relational Database - ANSWER-What is a collection of data items with predefined
relationships between them e.g. collection of tables.
Data Sources - ANSWER-In house, open Data, web server, data lake, data warehouse,
self-generated are types of what?
