Course 1, Module 4

Question 1

What is fairness?

Answer 1

Means ensuring your analysis doesn’t create or reinforce bias. If the analysis is not objective, the conclusions can be misleading and even harmful.

Question 2

What are some strategies that support fair analysis?

Answer 2

1. Consider all of the available data - Can’t ignore irrelevant data; it’s critical to consider all of the available data so that your analysis reflects the truth and not just your own expectations.
2. Must consider the complicated social contexts that could create bias, prioritize fairness during data collection, and ensure anything shared with stakeholders is presented in context.
3. Include self-reported data
4. Use oversampling effectively - Oversampling is the process of increasing the sample size of nondominant groups in a population. This can help you better represent them and address imbalanced datasets.
5. Think about fairness from beginning to end

Question 3

Define issue, question, problem?

Answer 3

An issue is a topic or subject to investigate.

A question is designed to discover information.

A problem is an obstacle or complication that needs to be worked out.

Question 4

What is a business task?

Answer 4

The question or problem data analysis answers for a business.

Question 5

Name a few careers related to data that one could pursue?

Answer 5

Data analyst,
Business analyst,
Data analytics consultant
Data engineer,
Data scientist,
Data specialist,
Operations analyst,

Industry-specific specialist positions that you might come across in your data analyst job search include:

Marketing analyst,
HR/payroll analyst,
Financial analyst,
Risk analyst,
Healthcare analyst

Question 6

What are some interview best practices?

Answer 6

Go check downloaded video.