Ways to Ensure Data Integrity | Google Data Analytics Certificate

Let’s talk about how to make sure that your organized data is complete and accurate. Clean data is the key to making sure your data has integrity before you analyze it. We’ll show you how to make your data clean and tidy.
0:00 Welcome to Data Integrity
4:22 Why Data Integrity is so Important
7:15 Balancing Objective and Integrity
10:31 Dealing with Insufficient Data
14:22 The Importance of Sample Size
17:51 Using Statistical Power
22:51 Determine Sample Sizes Through Population and Purpose
27:40 Reliable Data
This video is part of the Google Data Analytics Certificate which teaches learners how to prepare, process, analyze, share, and act on data.
The program, created by Google employees in the field, is designed to provide you with job-ready skills in about 6 months to start or advance your career in data analytics.
Take the Certificate HERE: goo.gle/3YZJx1Z
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[@inhlam5909]

0:00 Welcome to Data Integrity
4:22 Why Data Integrity is so Important
* Data integrity: accuracy, completeness, consistency, and trustworthiness of data throughout its lifecycle
* Data can be compromised: Data replication, Data transfer, Data manipulation,...
7:15 Balancing Objective and Integrity
10:31 Dealing with Insufficient Data
Types of Insufficient Data:
* Only one source >> missing the overall, also not enough data.
* Data keeps updating.
* Outdated data.
* Geographically-limited data.
Dealing:
* Find more data sources.
* Collect more data if time allows.
* Talk to stakeholders, and adjust the objective.
* Look for new data set.
14:22 The Importance of Sample Size
* If collecting the whole population is too time-consuming and expensive.
* So collecting a sample size of the population could represent the whole: effectively time and cost.
17:51 Using Statistical Power
* Perform on a "larger" sample size
* Hypothesis testing: survey/experiment to get the meaningful results
* And using "Confidence level" to determine the results of an experiment from hypothesis testing.
* At least 0.8 or 80% confidence level proven results the hypothesis testing.
22:51 Determine Sample Sizes Through Population and Purpose
* Focus on the big picture: get a proper sample size make the sense, and lead valid and useful.
* To find a sample size, need to know: the population size, confidence level, and margin of error.
>> Note: confidence level and margin of error do have to not add up to 100%, they are not dependent on each other.
27:40 Reliable Data

[@uolaod]

So valuable tutorial I belive i will a master in data analysis

[@ekkamailax]

Excellent video. Using this to help clean up a massive sales lead list.

[@notsure1320]

Thank you. This was insightful.

[@aminsherzad638]

Thank you for this awesome tut. Could you please have another dedicated video for Sampling - detailed? If you've such a tut already, can you please share the link? Thanks in advance.

[@minjinjargalsaikhan7325]

I just love her. The best explanation!

[@ag-88802]

Great video and information.

[@imperador7053]

Thanks for this insight

[@nanasophia6242]

Thank you so much ❤️

[@h_4943]

when it comes to margin of error isn't it that when you are doing two way testing as in the case of the 4 day work week the error is split and not 50% or 70% margin? so margin of error OR alpha = 0.1 then 0.05 would be for the left side of the confidence interval and 0.05 for the right side? this would make the margin actually 55% or 65% right?

[@rahimiramly433]

How to clean my data

[@aravindanv8211]

I'm a recent graduate Mechatronics engineer. I'm interested to work with Google. I applied so many jobs at Google. But I didn't get even a single opportunity.. I'm looking for a opportunity.

[@alienboogieman]

Lol, did you find one?

[@udcentrepreneur2566]

I don't mean to be rude, but she looks like a cute Chines I.A. Robot