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Critical Thinking is the most important skill in Data Science so that it is a must-have skill for data analysts. Critical thinking is so important in data science because:
         i) Critical thinking offers useful hindsight and effective principles to face the challenges of volume, velocity, variety and veracity of big data, and critical thinking is an important cognitive process in extended data mining.
         ii) Critical thinking (and cognitive process in general) plays an important role in data mining functionalities (or tasks).

There are many different ways to define Critical Thinking. The Critical Thinking is a manner of thinking that employs curiosity, creativity, skepticism, analysis, and logic, where:
         i) Curiosity means wanting to learn.
         ii) Creativity means viewing information from multiple perspectives.
         iii) Skepticism means maintaining a "trust buy verify" mind-set.
         iv) Analysis means systematically examining and evaluating evidence.
         v) Logic means reaching well-founded conclusions.

Citical thinking skills in regard to ethics will give data scientists the information they need to make more informed decisions about how to direct company strategy. Linda Burtch at Burtch Works [1] said those who have a well-developed understanding of data analytics should be in charge of running companies.

Strategies for critical thinking are:
         i) Challenge all assumptions.
         ii) Make the right decision for the majority.
         iii) Be a continuous learner and explain how new information can change a problem.
         iv) Listen and consider unconventional opinions.
         v) Avoid analysis paralysis and suspend judgment.
         vi) Revise conclusions based on new evidence.
         vii) Emphasize data over beliefs.
         viii) Never-end testing of ideas and identify new information that might support or contradict a hypothesis.
         iv) Fresh perspective that mistakes are data.
         ix) Use the earnest consideration of possibilities and ideas without (always accepting) them.
         xi) Look for what other have missed.
         xii) Compare two things (show similarities) and identify alternative interpretations for data or observations.
         xiii) Contrast two things (show differences).
         xiv) Analyze a topic (break into its parts).
         xv) Categorize something (tell what type it is).
         xvi) Evaluate something (explain its value or worth).
         xvii) Analyze yourself. The analysts with critical thinking develop a skill for explaining to others why they came to a specific conclusion. Others can follow their reasoning and can understand what they think. They are willing to change their views when they are provided with more information that allows greater understanding.

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[1] Bill Franks, 97 Things About Ethics Everyone in Data Science Should Know, 2020.
[2] Chen Z, Behavior mining for big data: promoting critical thinking in data science education. In: Proceedings of FEC, pp 337–341, 2014.