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DSA training part 1

Total questions: 8

Worksheet time: 6mins

Name
Class
Date
1.

Which of the following have we discussed as fundamentals of Data Science & Analytics?

a)

Computer Science

b)

Domain Knowledge

c)

Statistics

d)

Scientific Method

2.

What does it mean when a data science model has been trained?

a)

The computer has learned the best possible relation between the input data and the output data.

b)

The computer knows.

c)

The selected parameters/settings minimize the errors the model makes on the test data.

d)

The selected model parameters/settings have minimized the errors the model makes on the training data.

3.

How many AI winters have we had?

a)

0

b)

1

c)

2

d)

3

4.

Which of the following is not a reason why Data Science & Analytics are popular again?

a)

Global rise in scientists

b)

The rise of big data

c)

Improved Data Science & Analytics techniques

d)

Available Infrastructure & Platform Tools

5.

Select the levels of analytics in the right order of increased complexity/value from low to high.

a)

Diagnostic, Descriptive, Predictive, Prescriptive

b)

Descriptive, Diagnostic, Predictive, Prescriptive

c)

Descriptive, Diagnostic, Prescriptive, Predictive

d)

Diagnostic, Descriptive, Prescriptive, Predictive

6.

With respect to new developments, what does the abbreviation GAN stand for?

(a)  

7.

Select the limitations of deep-learning.

a)

It is expensive (Electricity/equipment)

b)

Specialist knowledge is required

c)

It is hard to understand the final outcome

d)

You need to collect large amounts of data

8.

What are some pitfalls of the democratization of data science?

a)

Data is valuable and needs gatekeeping (Ensure proper Privacy & Security)

b)

Data is often improperly used or interpreted (Ensure proper domain knowledge)

c)

There is not enough documentation available for the tools.

d)

Bringing a system to production is much harder than a demo (Ensure proper development)