Explanation:
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A part-time job would provide on-the-job experience while also giving him a flexible schedule competing with school.
Answer:
#program in Python
#read until user Enter an integer
while True:
#try block to check integer
try:
#read input from user
inp = int(input("Enter an integer: "))
#print input
print("The integer is: ",inp)
break
#if input is not integer
except ValueError:
#print message
print("Wrong: try again.")
Explanation:
In try block, read input from user.If the input is not integer the print a message in except block.Read the input until user enter an integer. When user enter an integer then print the integer and break the loop.
Output:
Enter an integer: acs
Wrong: try again.
Enter an integer: 4a
Wrong: try again.
Enter an integer: 2.2
Wrong: try again.
Enter an integer: 12
The integer is: 12
Simply a glare screen, because it clearly states what it protects one against, I guess.
Answer:
2. A data modelling project using a packaged data model REQUIRES A GREATER SKILL than a project not using a packaged data model.
Explanation:
1a. Review of universal models:
A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real world entities. It has become the standard approach used towards designing databases.
A universal data model is a template data model that can be reused as a starting point or a building block to jump-start development of a data modelling project, industry specific model, logical data models.
1b. Discuss how these are being used more widely today.
*Universal data models helps professional reduce development time, improve consistency and standardization while achieving high quality models.
*Higher quality: just as architects consider blue prints before constructing a building, one should also consider data before building an app. A data model helps define the problem, enabling one to consider different approaches and choose best ones.
*By properly modelling and organization's data, the database designer can eliminate data redundancies (needless repetitions) which are a key source for inaccurate information and ineffective systems.
2. Greater and advanced skills are adequate and required when data modelling project is done using packaged data model while fewer skills are required when data modelling is done without packaged data model.
