1 Introduction to Data Science Free Online FlashCards

What should guide a data science project before tools are chosen?

Begin by clarifying the question, who will use the result, and what evidence would count as a useful answer.

What should be documented when acquiring data?

Obtain data from appropriate sources and record where it came from and how it was obtained.

What should you examine to understand a dataset?

Check the data’s structure, meaning, coverage, and limitations.

What are examples of data preparation?

Correct errors, handle missing values, and combine relevant sources as needed.

How should a team choose analysis or modeling methods?

Choose analysis or modeling methods suited to the question being answered.

What does evaluation check in a data science project?

Check whether the results are reliable and actually address the original purpose.

Why is the data science workflow not a one-way checklist?

The workflow is iterative: analysis can expose data problems, and evaluation can prompt a revised question or approach.

What is CSV particularly convenient for?

CSV stores rows and fields as text, making it convenient for simple tables and widely supported.

What information should a data schema or dictionary provide?

A schema or data dictionary explains field names, types, units, and meanings so the values can be interpreted in context.

What does data minimization require?

Collect only what is needed for a defined purpose, and keep personal information only as long as necessary.

What should you consider about who is represented in a dataset?

Check whether relevant groups or circumstances are omitted, since gaps in representation can affect conclusions.

What privacy risk should be checked before sharing data?

Consider whether people could be identified from the data or combinations of details, and share only what is appropriate for the stated purpose.