DSA training part 1

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

• Computer Science

• Domain Knowledge

• Statistics

• Scientific Method

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

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

• The computer knows.

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

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

How many AI winters have we had?

• 0

• 1

• 2

• 3

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

• Global rise in scientists

• The rise of big data

• Improved Data Science & Analytics techniques

• Available Infrastructure & Platform Tools

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

• Diagnostic, Descriptive, Predictive, Prescriptive

• Descriptive, Diagnostic, Predictive, Prescriptive

• Descriptive, Diagnostic, Prescriptive, Predictive

• Diagnostic, Descriptive, Prescriptive, Predictive

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

Select the limitations of deep-learning.

• It is expensive (Electricity/equipment)

• Specialist knowledge is required

• It is hard to understand the final outcome

• You need to collect large amounts of data

What are some pitfalls of the democratization of data science?

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

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

• There is not enough documentation available for the tools.

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