1. A
data analyst is working on an urgent traffic study. As a result of the short
time frame, which type of data are they most likely to use?
- Unclean
- Theoretical
- Personal
- Historical
Explanation: It is quite
possible that the data analyst will make use of real-time or near-real-time
data while conducting an urgent traffic study in which time is a particularly
important component. It is possible for the analyst to make judgments that are
both timely and well-informed when using this sort of data since it gives the
most recent information on the circumstances of the traffic. This might include
traffic sensors, GPS data from automobiles, or live feeds from traffic cameras.
Real-time data sources could also include satellite imagery. The analyst is
able to react swiftly to the present traffic condition and handle the urgent
nature of the research when they make use of such data.
2.
Which of the following is an example of continuous data?
- Movie budget.
- Movie run time.
- Leading actors in movie.
- Box office returns.
3.
Nominal qualitative data has a set order or scale.
- True
- False
Explanation: In
point of fact, nominal qualitative data does not have a predetermined or
predetermined scale. There is a form of categorical data known as nominal data.
In nominal data, the categories reflect different groups or labels, but these
categories do not have any intrinsic order or ranking.
For instance, various colors (such as red, blue, and green)
or categories of fruit (such as apple, orange, and banana) are examples of
nominal data. There is no logical order or scale among these categories; on the
contrary, they are only different names.
Ordinal data, on the other hand, is a sort of categorical
data in which the categories are arranged in a meaningful order or ranking. As
an example of ordinal data, consider a survey question that allows respondents
to choose their answer from the following options: "strongly
disagree," "disagree,"
"neutral," "agree," and "strongly
agree." In this particular instance, the categories are arranged in a
logical fashion.
4.
Which of the following is a benefit of internal data?
- Internal data is less likely to need cleaning.
- Internal data is less vulnerable to biased collection.
- Internal data is the only data relevant to the problem.
- Internal data is more reliable and easier
to collect.
Explanation: Internal data is
data that is created inside an organization, and it offers a high degree of
customisation and specificity to the procedures, activities, and objectives of
the business. This data is adapted to the particular requirements and
circumstances of the company, which enables in-depth analysis and
decision-making to achieve the desired results.
5.
Structured data is likely to be found in which of the following formats? Select
all that apply.
- Audio file
- Digital photo
- Spreadsheet
- Table
6.
Which of the following values are examples of a Boolean data type? Select all
that apply.
- Yes, no, or unsure
- Yes or no
- One, two, or three
- True or false
7. The
following is a selection from a spreadsheet:
- Table
- Narrow
- Wide
- Long
- Short
8. Data
transformation can change the structure of the data. An example of this is
taking data stored in one format and converting it to another.
- True
- False
Explanation: Absolutely!
Changing the structure or format of data is an example of data transformation.
Converting data from one format to another is a frequent example of data
transformation. This may include transforming data from one data type to
another, such as translating text data to numerical format, or converting data
from a raw, unprocessed state to a format that is more structured and useable.
For example, converting a CSV file to a relational database is an example of
this. One of the most important steps in the process of preparing data is
called data transformation. This stage ensures that the data are ready for
analysis and reporting.
9.
Which of the following questions collect nominal qualitative data? Select all
that apply.
- Have you heard of our frequent diner
program?
- How likely are you to recommend this restaurant to a friend?
- Is this your first time dining at this restaurant?
- Did anyone recommend our restaurant to you today?
10. A
social media post is an example of structured data.
- True
- False
Explanation: Unstructured
data is frequently represented by a social media post, an example of this kind
of data. The data that is considered to be unstructured does not possess a data
model that has been pre-defined and needs to be arranged in a manner that is
readily understandable by computers.
Posts on social media platforms often consist of various
components, including text, photographs, videos, emoticons, and other features.
Even though the post may have some aspects of structure, such as a date, a
username, and text, the format as a whole is somewhat flexible and needs to be
arranged in a tabular or highly structured fashion.
On the other hand, structured data is arranged in a
particular manner, such as tables in a relational database or rows and columns
in a spreadsheet. This kind of data is differentiated from unstructured data.
Examples of structured data include spreadsheets, databases, and CSV files,
among other data types.
11. A
Boolean data type must have a numeric value.
- True
- False
Explanation: This
is not always the case. There are normally two values that are represented by a
Boolean data type: true and false. In certain computer languages or systems,
true and false may be internally represented as numeric values (1 for true and
0 for false). However, the most important characteristic of Boolean data is its
binary nature, which means that there are only two possible values.
Several programming languages allow you to use the keywords
"true" and "false" without having to give precise numeric
values to them. This is possible in many of these languages. A binary condition
is represented by Boolean data, which means that anything is either true or
false. This is the most crucial thing to keep in mind.
12. In
long data, separate columns contain the values and the context for the values,
respectively. What does each column contain in wide data?
- A specific data type
- A unique data variable
- A specific constraint
- A unique format
Explanation: Wide
data normally consists of rows that hold the values for the variables that are
represented in the columns, and each column typically represents a variable or
a characteristic. Wide data is a kind of data that organizes the information in
a horizontal fashion, with each column being devoted to a particular variable.
This is in contrast to long data, which is organized in distinct columns that
include values and the context for the values (for example, variable names).
13. A
data analyst is working in a spreadsheet application. They use Save As to
change the file type from .XLS to .CSV. This is an example of a data
transformation.
- True
- False
Explanation: The use of the
"Save As" command to convert a file from the Excel format (.XLS) to
the Comma-Separated Values (.CSV) format is, in fact, an example of a data
transformation. In this particular instance, the data is being converted from
one file type to another data format.
For the purpose of the transformation, the data from the
spreadsheet will be converted into a plain text format, with commas serving as
the separator between each data item. This format is more lightweight than the
Excel format, which makes it simpler to transfer and work with in a variety of
apps that handle CSV files. It is often used for data interchange and is more
commonly used than Excel.
14.
If you have a short time frame for data collection and need an answer
immediately, you likely will have to use historical data.
- True
- False
Explanation: This
is not always the case. It's possible that using historical data isn't the best
choice if you have a limited amount of time to gather data and you need an
answer right away. The term "historical data" refers to information
that has been gathered over a period of time, and therefore may not be enough
for delivering responses in real time or right away.
In circumstances when time is of the essence, you would
normally depend on data sources that provide information in real time or near
real time in order to get the most recent relevant information. Live data
streams, sensors, and other sources that give rapid insights might be included
in this category.
Although historical data is useful for analyzing trends,
recognizing patterns, and gaining a knowledge of long-term patterns, it may not
be the ideal option when information that is both urgent and real-time is
required.
15.
Continuous data is measured and has a limited number of values.
- True
- False
Explanation: In point of fact, continuous data is
determined by measurement and may take on an endless variety of values within a
certain range. When we talk about continuous data, we are referring to
measurements that can be broken down into smaller units with a higher degree of
accuracy. Additionally, it is theoretically capable of being measured with an
unlimited degree of accuracy and can take any value within a range.
For instance, height is an example of continuous data. It
is feasible to measure height with a high degree of accuracy, and there is no
restriction on the number of alternative values that may be found within a
certain range. Discrete data, on the other hand, is counted and consists of a
collection of values that are distinct and independent from one another.
16. Internal data is more reliable
because it’s clean.
- True
- False
Explanation: Internal
data may offer benefits in terms of accessibility and familiarity;
nevertheless, the notion that it is more trustworthy just because it is
internal or clean is not always accurate. Internal data may have advantages
related to familiarity and accessibility. The dependability of data is
contingent upon a number of elements, such as the method by which the data is
collected, the procedures that are followed for data management, and the
environment in which the data is used.
If appropriate data quality standards are in place and a
well-established data governance system is in place, then the data that is
collected internally may be considered credible. Nevertheless, it is of the
utmost importance to acknowledge that data, regardless of whether it is
internal or external, might be subject to problems such as mistakes, biases, or
inconsistencies. It is the quality assurance procedures that are used
throughout the data collecting, storage, and analysis processes that have an effect
on the dependability of the data.
When validating and verifying the trustworthiness of data
sources, it is essential to take into consideration aspects such as
correctness, completeness, and consistency. This applies to both internal and
external data sources. The methods of data cleansing, verification, and
validation are essential measures that must be taken in order to guarantee the
dependability of the data that is used for assessment or decision-making.
17.
A social media post is an example of structured data.
- True
- False
Explanation: A
post on social media is not an example of structured data; rather, it is not
structured data. The absence of a predetermined data model and the absence of
an organization that is readily accessible by computers are both
characteristics of unstructured data.
It is common for a post on social media to include a variety
of multimedia components, including text, photographs, videos, emoticons, and
other features. In spite of the fact that the post may have some structure (for
example, timestamps, usernames, and hashtags), the format as a whole is
flexible and does not adhere to a rigid, predetermined framework.
The opposite of unstructured data is structured data, which
is arranged in a specified and highly formatted fashion. For example, tables in
a relational database or rows and columns in a spreadsheet are examples of
structured data. Examples of structured data include spreadsheets, databases,
and CSV files, among other types of data.
18.
A data analyst at a book publisher is working on an urgent report for
executives. They are using only historical data. What is the most likely reason
for choosing to analyze only historical data?
- The data is constantly changing
- There is plenty of time to research historical data
- The project has a very short time frame
- The data is unknown
19.
Which of the following is an example of continuous data?
- Box office returns
- Movie run time
- Movie budget
- Leading actors in movie
20. Why
is internal data considered more reliable and easier to collect than external
data?
- Internal data circumvents privacy restrictions.
- Internal data has much larger sample sizes.
- Internal data lives within a company’s own
systems.
- Internal data comes from people you know.
21.
Which of the following is an example of structured data?
- Digital photo
- Relational database
- Audio file
- Video file
22. In
long data, separate columns contain the values and the context for the values,
respectively. What does each column contain in wide data?
- A specific data type
- A unique format
- A unique data variable
- A specific constraint
Explanation: Wide
data normally consists of rows that include the values that correlate to the
variables that are contained in each column, and each column typically contains
a variable or a feature. The difference between wide data and long data is that
with wide data, each column represents a different variable or measure, but in
long data, each column represents a context or characteristic.
23.
Which of the following questions collects nominal qualitative data?
- On a scale of 1-10, how would you rate your service today?
- Is this your first time dining at this
restaurant?
- How many times have you dined at this restaurant?
- How many people do you usually dine with?
24.
Nominal qualitative data has a set order or scale.
- True
- False
Explanation: The solutions to this question are divided into many categories, and there is no predetermined order or ranking among them within the question itself. The data is said to be nominal qualitative since each category is handled as if it were its own label.