Ensuring Credibility in Your Data | Google Data Analytics Certificate 

1. Ensuring Credibility in Your Data 2. From Questions to Conclusions: * Consider the Bias and Fairness of the data from collection to conclusion * Data bias (bias real impacts when apply to the world) 3. Biased and Unbiased Data * Sampling bias (not representing the population as the whole) --> can be handy in narrowing the focus to get a solution. * Unbiased sampling (represent the population being measured) --> to get this chosen randomly. >> An easy way to view bias or unbias that visualize data 3.1. Understanding Biases in Data: types of data bias * Sampling bias * Observer bias (experimenter/research bias): different people observe from a different view * Interpretation bias: interpret the ambiguous situation in a positive or negative way (see and hear the same thing but in different ways) * Confirmation bias: in a way that confirms pre-existing beliefs. >>> Inspective for accuracy and trustworthiness 3.2. Identify valid data sources: Who created the data set * ROCCC (ROCK): Reliable, Original, Comprehensive, Current, Cited 3.3. What bad data: * Bad data sources don't ROCCC >>> Avoiding the bad data you can find a good solution. 3.4 . Introduction to Data Ethics: Ethics, Data Ethics, GDPR * Aspects of Data Ethics: Ownership, Transaction Transparency, Consent, Currency, Privacy, and Openness. 4. Introduction to Data Privacy: 22:30, access, usage, and share data 5. Accessibility with Open Data: use, reuse, and redistribution Notes: * Analyze data for bias and credibility * Good data vs. bad data * Data ethics, privacy, and access

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