Understanding Data Types and Structures | Google Data Analytics Certificate

[@inhlam5909]

1. Welcome to Data Types and Structures 2. Data collection in our world: Interviews, Observations (animal behavior, bacteria under a microscope), Forms, Questionaries, and Surveys. Know how to generate data - know the context, and know-how to collect data - analyze data more efficiently. 3. Determining What Data to collection How to collect, Choose data sources (First-party data, Second-party data), Decide what data to use, How much data to collect (sample - a part data representative of the full data), Select the right data type, Determine the time frame 4. Data Formats: * Quantitative: Discrete (format in limited) and Continuous data (Any numeric value) * Qualitative: Nominal (yes/no/not sure, ..) and Ordinal data (order/scale: 1-5, A-D, ..) * Internal data (company's own system) and External data (outside of an organization) * Structured data (searchable, more analysis) and Unstructured data (not organized, etc. Audio Files, Video Files, Emails, Photos, and Social Media) 5. Understanding Structured Data: Data Models, Data Elements * Sources of Structured data: Spreadsheets and Databases that store datasets. 6. Types of Data vs. Data Types: * Data types in Spreadsheets: 1. Number, 2. Text or string, 3. Boolean 7. Data Table components: * Rows -> Records * Columns -> Fields Cons: Records and Fields in any kind of data table, while Rows and Columns in spreadsheets 8. Different Data Formats: * Wide data (Single row multiple columns): compare and identify quickly. * Long data (One row one column is the one-time point)

[@siwabuk]

Thank you so much for making this recap playlist, I've finished this certificate but want to brush up on this knowledge. It is easier to rewatch on this playlist rather than on Coursera. Thanks again.

[@robinpuerta]

Quantitative/Qualitative + Discrete/Continuous + Nominal/Ordinal + Internala/xternal + Structured/Unstructured + Wide/Long. Got it

[@TheM3thoD]

This is really helpful, thank you so much.

[@emmanuelmorris812]

Very interesting.