DATA LITERACY TEST QUESTIONS AND ANSWERS #1
What is data science? - correct answer The field of study that combines domain
expertise, programming skills, and knowledge of math and statistics to extract
meaningful insights from data.
What are some central components within data science? - correct answer Capture
information - develop methods of capture and collect important data.
Store and maintain data - Create data stores that are compatible with how data is being
collected and it is intended method of consumption. Frequently revise and maintain data
stores to ensure quality and reliability.
Conform data for Analysis - Transform existing data stores for planned analysis,
conforming data into required layouts.
Analysis - Work with subject matter experts determine which tool or technique can
effectively uncover desired insights.
Communicate results - Summarize and transmit findings in an easily digestible way so
that insights can be understood by all levels of the organization.
What 3 key areas are important for data scientists? - correct answer Domain Expertise:
subject matter experts with an intimate knowledge of the problem at hand.
Programming Skills: People with access to advanced computing resource and
techniques.
Statistical methodology: Knowledge of current and developing statistical and analytical
techniques.
Does data storytelling relate to data literacy? - correct answer Yes. All data literacy
aspects (read, work with, analyze and argue with data) are improved by applying
different aspects of data storytelling.
What is data storytelling? - correct answer Data storytelling is designed to help convey a
message, trend, analysis etc. To an audience, helping them connect to the data and the
information.
The essence of data storytelling is helping individuals understand what is happening in
the data and sea of information, making it easier to access the right information and
signals, so that individuals don't have to sift through mounds and mounds of data, to
find an answer. A story itself can bring this home.
What are the three U's in data Storytelling? - correct answer · Understanding your data
· Understand the business question
,· Understand the audience
Describe the data story telling U: Understanding your data
- What is it?
- Why is it important? - correct answer Can be divided into backend (how the data is
created, extracted and transformed) and (frontend what measure and visualization we
use, what is the message of the visualizations).
Backend is important for the accuracy of the data. Frontend is important to provide
value.
Describe the following data storytelling tips
Simplicity
Connection
Utilize data visualization and dashboards
Context - correct answer · Simplicity
The world of data, statistics and analytics can be very complicated and complex.
· Connection
A connection to the data through a story allows each individual and the whole audience
to feel the data is personal to them and their own. Work hard to create this connection.
· Utilize data visualization and dashboards
Images are easier to understand than a list of facts and figures.
· Context
Provide context. Discuss the data you used and why you chose the data sets you did.
What does the term "make the data human" mean in data storytelling context? - correct
answer · One of the most important points in data storytelling.
· When thinking of data, statistics and analytics, an overall feeling of some is that it is
intimidating and hard.
· Data storytelling can allow individuals to connect to data in ways that they never knew
were possible.
What is signal and noise?
- Name some examples of signal and noise - correct answer · Signal and noise
represent the concept in analytics in dealing with finding the correct pieces of
information, among a sea of potentially distracting and unhelpful information.
, Examples:
· Job Interview - what do they really want with a question?
· Doctors Visits - what is wrong and what do I need to do?
· Marketing Campaigns - identify what customers truly are saying about your products.
Why is the concept of signal and noise important today? - correct answer · With the
increasing amount of data and noise, to become increasingly efficient in identifying
signals is becoming increasingly important, both as a personal skill and for companies.
What are four essential skills to becoming better at differentiating signals from noise? -
correct answer o Skill #1: Ask the question: Determine what is being asked
§ This question can be asked even before building a data model.
§ We should know our goal and strategy, regardless if they are my own, team, or
company goal.
§ By understanding what we look for, we can take this into the questions of identifying
the signal.
O Skill #2 Leave bias behind
§ Remove preconceived opinions
§ Avoid clouding your judgement.
§ Look objectively
O Skill #3 Be skeptical
§ Ask questions such as "Why are the numbers coming in this way? Does this answer
what we are looking for?
O Skill #4 why?
§ See beyond the numbers
§ Find meaning
§ Tie to the objective
§ Open up your analysis
What is Analytics?
Is reporting Analytics? - correct answer Using data and information to make informed
decisions.
§ What campaign was more effective?
§ Where should I be focusing our budget this year?
O Answering the why?
§ Why was the sells down a third?
O What Analytics is not
§ Analytics is not reporting. Reporting gives data. Analytics gives answers. Analytics
can give you the "why" behind the data.
What is data science? - correct answer The field of study that combines domain
expertise, programming skills, and knowledge of math and statistics to extract
meaningful insights from data.
What are some central components within data science? - correct answer Capture
information - develop methods of capture and collect important data.
Store and maintain data - Create data stores that are compatible with how data is being
collected and it is intended method of consumption. Frequently revise and maintain data
stores to ensure quality and reliability.
Conform data for Analysis - Transform existing data stores for planned analysis,
conforming data into required layouts.
Analysis - Work with subject matter experts determine which tool or technique can
effectively uncover desired insights.
Communicate results - Summarize and transmit findings in an easily digestible way so
that insights can be understood by all levels of the organization.
What 3 key areas are important for data scientists? - correct answer Domain Expertise:
subject matter experts with an intimate knowledge of the problem at hand.
Programming Skills: People with access to advanced computing resource and
techniques.
Statistical methodology: Knowledge of current and developing statistical and analytical
techniques.
Does data storytelling relate to data literacy? - correct answer Yes. All data literacy
aspects (read, work with, analyze and argue with data) are improved by applying
different aspects of data storytelling.
What is data storytelling? - correct answer Data storytelling is designed to help convey a
message, trend, analysis etc. To an audience, helping them connect to the data and the
information.
The essence of data storytelling is helping individuals understand what is happening in
the data and sea of information, making it easier to access the right information and
signals, so that individuals don't have to sift through mounds and mounds of data, to
find an answer. A story itself can bring this home.
What are the three U's in data Storytelling? - correct answer · Understanding your data
· Understand the business question
,· Understand the audience
Describe the data story telling U: Understanding your data
- What is it?
- Why is it important? - correct answer Can be divided into backend (how the data is
created, extracted and transformed) and (frontend what measure and visualization we
use, what is the message of the visualizations).
Backend is important for the accuracy of the data. Frontend is important to provide
value.
Describe the following data storytelling tips
Simplicity
Connection
Utilize data visualization and dashboards
Context - correct answer · Simplicity
The world of data, statistics and analytics can be very complicated and complex.
· Connection
A connection to the data through a story allows each individual and the whole audience
to feel the data is personal to them and their own. Work hard to create this connection.
· Utilize data visualization and dashboards
Images are easier to understand than a list of facts and figures.
· Context
Provide context. Discuss the data you used and why you chose the data sets you did.
What does the term "make the data human" mean in data storytelling context? - correct
answer · One of the most important points in data storytelling.
· When thinking of data, statistics and analytics, an overall feeling of some is that it is
intimidating and hard.
· Data storytelling can allow individuals to connect to data in ways that they never knew
were possible.
What is signal and noise?
- Name some examples of signal and noise - correct answer · Signal and noise
represent the concept in analytics in dealing with finding the correct pieces of
information, among a sea of potentially distracting and unhelpful information.
, Examples:
· Job Interview - what do they really want with a question?
· Doctors Visits - what is wrong and what do I need to do?
· Marketing Campaigns - identify what customers truly are saying about your products.
Why is the concept of signal and noise important today? - correct answer · With the
increasing amount of data and noise, to become increasingly efficient in identifying
signals is becoming increasingly important, both as a personal skill and for companies.
What are four essential skills to becoming better at differentiating signals from noise? -
correct answer o Skill #1: Ask the question: Determine what is being asked
§ This question can be asked even before building a data model.
§ We should know our goal and strategy, regardless if they are my own, team, or
company goal.
§ By understanding what we look for, we can take this into the questions of identifying
the signal.
O Skill #2 Leave bias behind
§ Remove preconceived opinions
§ Avoid clouding your judgement.
§ Look objectively
O Skill #3 Be skeptical
§ Ask questions such as "Why are the numbers coming in this way? Does this answer
what we are looking for?
O Skill #4 why?
§ See beyond the numbers
§ Find meaning
§ Tie to the objective
§ Open up your analysis
What is Analytics?
Is reporting Analytics? - correct answer Using data and information to make informed
decisions.
§ What campaign was more effective?
§ Where should I be focusing our budget this year?
O Answering the why?
§ Why was the sells down a third?
O What Analytics is not
§ Analytics is not reporting. Reporting gives data. Analytics gives answers. Analytics
can give you the "why" behind the data.
