Study Guide for WGU Introduction to Analytics - D491 Section 1
Exam Questions and well elaborated Answers 100% Guaranteed Pass
1. data analytics: a coming together to solve a business problem through the creative
use of data and statistical modeling to tell a compelling story that drives strategic action
and results in business value
2. data science: a field of study that involves using computational and statistical
techniques to extract insights and knowledge from data
3. variables: a container or storage location that holds a value; They can be manip- ulated
throughout the data analysis process to achieve the necessary results
4. Descriptive Analytics: Within a project, this type of analytics focuses on summa- rizing and
describing historical data to provide insights into past trends and patterns. This helps to
answer questions such as "What happened?" and "How many times did it happen?"
5. Diagnostic Analytics: analyzes past data to identify the root causes of specific outcomes
or events. It answers questions such as "Why did an event happen?
6. Predictive Analytics: Using historical data to forecast future outcomes, It helps to answer
questions such as "What is likely to happen in the future?" and "How can we prepare for it
7. Prescriptive Analytics: This type of analytics recommends actions that can be taken to
optimize or improve a situation. It considers data analysis and modeling to provide specific
recommendations on what to do next
8. Exploratory Analytics: This type of analytics involves exploring and analyzing data to
identify potential trends, patterns, and relationships. It is often used when there is no clear
objective or question to answer, and the goal is to uncover new insights or opportunities.
9. Data analysts' responsibilities/ primary goal: to collect, analyze, and interpret datasets.
,Depending on the industry, they hold various responsibilities within organi- zations, such as
collecting data; cleaning, preparing, and processing data; analyzing data; reporting and
visualizing data; implementing predictive analysis; identifying data-driven decisions; and
engaging in continuous improvement.
One of their primary goals is to improve business decisions and outcomes based on
interpreting datasets
10. Data collection: Collecting and gathering large datasets, including databases, surveys,
and other data sources from various platforms
11. Data cleaning, preparation, and processing: Cleaning and preparing datasets for analysis
by correcting data errors, removing duplicates, and reviewing accuracy; these processes are
also known as data filtering, data integration, data classifica- tion, data munging, and data
summarization
, 12. Data analysis: Utilizing statistical methods and data visualization tools to an- alyze
large datasets, identify trends, and compile insights to assist organizations when making
business decisions
13. Data reporting and visualization: Creating clear, concise reports and visuals that easily
communicate findings to team members and key stakeholders
14. Predictive analysis: Using algorithms (sets of detailed instructions that are used to
solve specific problems or calculate specific operations) to assist with predicting trends
and future outcomes based on historical data
15. Data-driven decision-making: Collaborating with various team members and
stakeholders to identify opportunities for improvement to make data-driven deci- sions; you
could make a data-driven decision by analyzing the data on customer behavior and
preferences to identify which products are most popular and why
16. Continuous improvement: Involves professional development, continuously
monitoring and evaluating the effectiveness of decision-making processes, and
recommending improvements to drive better outcomes; data analysts must stay current
with the latest trends and tools within the field; continuous improvement is needed
because the field of data analytics is constantly evolving with new data sources, tools, and
techniques, and data analysts must be able to adapt to these changes in processes over
time
17. business intelligence analyst/ primary goal: primarily uses data to help busi- nesses
navigate daily decisions. they gather and organize datasets such as revenue, sales, market
trends, or customer engagement metrics. This allows the analysts to monitor and help
visualize data for key stakeholders effectively. One of the primary functions is locating
patterns or other trends that correlate to potential areas of improvement
Exam Questions and well elaborated Answers 100% Guaranteed Pass
1. data analytics: a coming together to solve a business problem through the creative
use of data and statistical modeling to tell a compelling story that drives strategic action
and results in business value
2. data science: a field of study that involves using computational and statistical
techniques to extract insights and knowledge from data
3. variables: a container or storage location that holds a value; They can be manip- ulated
throughout the data analysis process to achieve the necessary results
4. Descriptive Analytics: Within a project, this type of analytics focuses on summa- rizing and
describing historical data to provide insights into past trends and patterns. This helps to
answer questions such as "What happened?" and "How many times did it happen?"
5. Diagnostic Analytics: analyzes past data to identify the root causes of specific outcomes
or events. It answers questions such as "Why did an event happen?
6. Predictive Analytics: Using historical data to forecast future outcomes, It helps to answer
questions such as "What is likely to happen in the future?" and "How can we prepare for it
7. Prescriptive Analytics: This type of analytics recommends actions that can be taken to
optimize or improve a situation. It considers data analysis and modeling to provide specific
recommendations on what to do next
8. Exploratory Analytics: This type of analytics involves exploring and analyzing data to
identify potential trends, patterns, and relationships. It is often used when there is no clear
objective or question to answer, and the goal is to uncover new insights or opportunities.
9. Data analysts' responsibilities/ primary goal: to collect, analyze, and interpret datasets.
,Depending on the industry, they hold various responsibilities within organi- zations, such as
collecting data; cleaning, preparing, and processing data; analyzing data; reporting and
visualizing data; implementing predictive analysis; identifying data-driven decisions; and
engaging in continuous improvement.
One of their primary goals is to improve business decisions and outcomes based on
interpreting datasets
10. Data collection: Collecting and gathering large datasets, including databases, surveys,
and other data sources from various platforms
11. Data cleaning, preparation, and processing: Cleaning and preparing datasets for analysis
by correcting data errors, removing duplicates, and reviewing accuracy; these processes are
also known as data filtering, data integration, data classifica- tion, data munging, and data
summarization
, 12. Data analysis: Utilizing statistical methods and data visualization tools to an- alyze
large datasets, identify trends, and compile insights to assist organizations when making
business decisions
13. Data reporting and visualization: Creating clear, concise reports and visuals that easily
communicate findings to team members and key stakeholders
14. Predictive analysis: Using algorithms (sets of detailed instructions that are used to
solve specific problems or calculate specific operations) to assist with predicting trends
and future outcomes based on historical data
15. Data-driven decision-making: Collaborating with various team members and
stakeholders to identify opportunities for improvement to make data-driven deci- sions; you
could make a data-driven decision by analyzing the data on customer behavior and
preferences to identify which products are most popular and why
16. Continuous improvement: Involves professional development, continuously
monitoring and evaluating the effectiveness of decision-making processes, and
recommending improvements to drive better outcomes; data analysts must stay current
with the latest trends and tools within the field; continuous improvement is needed
because the field of data analytics is constantly evolving with new data sources, tools, and
techniques, and data analysts must be able to adapt to these changes in processes over
time
17. business intelligence analyst/ primary goal: primarily uses data to help busi- nesses
navigate daily decisions. they gather and organize datasets such as revenue, sales, market
trends, or customer engagement metrics. This allows the analysts to monitor and help
visualize data for key stakeholders effectively. One of the primary functions is locating
patterns or other trends that correlate to potential areas of improvement
