Which of the following is a common data analytics tool that is also used as an interpreted, high-level,
general-purpose programming language?
A.
SAS
B.
Microsoft Power BI
C.
IBM SPSS
D.
Python
Correct Answer: D
Explanation:
Python is a common data analytics tool that is also used as an interpreted, high-level, general purpose programming language. Python has a simple and expressive syntax that makes it easy to
read and write code. Python also has a rich set of libraries and frameworks that support various tasks
and applications in data analytics, such as data manipulation, visualization, machine learning, natural
language processing, web scraping, and more. Some examples of popular Python libraries for data
analytics are pandas, numpy, matplotlib, seaborn, scikit-learn, nltk, and beautiful soup. Python is
different from other data analytics tools that are not programming languages but rather software
applications or platforms that provide graphical user interfaces (GUIs) for data analysis and
visualization. Some examples of these tools are SAS, Microsoft Power BI, IBM SPSS. Therefore, the
correct answer is D. Reference: [What is Python? | Definition and Examples], [Python Libraries for
Data Science]
Question #7 (Topic: Demo Questions)
The number of phone calls that the call center receives in a day is an example of:
A.
continuous data.
B.
categorical data.
C.
ordinal data.
D.
discrete data.
Correct Answer: D
Explanation:
Discrete data is a type of data that can only take certain values, usually whole numbers or integers.
Discrete data can be counted, but not measured. For example, the number of students in a class, the
number of books in a library, or the number of phone calls that a call center receives in a day are all
examples of discrete data. Discrete data is different from continuous data, which can take any value
within a range, and can be measured with precision. For example, the height of a person, the weight
of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct
answer is D. Reference: [Discrete vs Continuous Data: Definition and Examples - Statistics How To],
[Discrete Data - Definition and Examples | Math Goodies]
Question #8 (Topic: Demo Questions)
A company’s marketing department wants to do a promotional campaign next month. A data analyst
on the team has been asked to perform customer segmentation, looking at how recently a customer
bought the product, at what frequency, and at what value. Which of the following types of analysis
would this practice be considered?
A.
Prescriptive
B.
Trend
C.
Gap
D.
Custer
Correct Answer: D
Explanation:
Customer segmentation is a type of cluster analysis, which is a method of grouping data points based
on their similarities or differences. Cluster analysis can help identify patterns and trends in the data,
as well as target specific groups of customers for marketing purposes. One common technique for
customer segmentation is RFM analysis, which stands for recency, frequency, and monetary value.
This technique assigns a score to each customer based on how recently they bought the product,
how often they buy the product, and how much they spend on the product. These scores can then be
used to create clusters of customers with different characteristics and preferences. Therefore, the
correct answer is D. Reference: Cluster Analysis - Statistics Solutions, RFM Analysis: The Ultimate
Guide for Customer Segmentation