Introduction to Data Science and Data Types

Practice flashcards covering the basics of data science, its process, components, and the various types and levels of data measurement.

What is the simple definition of Data Science?

Data Science is the process of converting raw data into useful information and knowledge for making better decisions.

What are the five steps in the Data Science Process?

Data Collection, Data Cleaning, Data Analysis, Visualization, and Decision Making.

Which three major areas are combined in the Data Science Venn Diagram?

Programming, Mathematics & Statistics, and Domain Knowledge.

Why is Domain Knowledge critical in Data Science?

Domain Knowledge involves understanding the specific field where data is used (e.g., healthcare or finance); without it, the analysis may be incorrect.

How does 'Information' differ from 'Data'?

Data refers to raw, unprocessed facts and figures, whereas Information is processed, organized, and meaningful data used for decision-making.

What is a 'Feature' or 'Attribute' in a dataset?

A characteristic describing data, such as Age, Salary, or Height.

What constitutes a 'Record' or 'Observation' in a dataset?

One complete row in a dataset, such as a single student's name, age, and marks combined.

What is 'Big Data'?

Very large datasets that cannot be processed using traditional software, such as Google Search records or YouTube videos.

What are the characteristics of Structured Data?

It is organized in tables (rows and columns), follows a fixed schema, and is easy to search or filter using SQL databases.

List four examples of Unstructured Data.

Images, Videos, Audio, and Emails.

What is 'Discrete Data'?

A type of quantitative data consisting of countable numerical values that are usually whole numbers and cannot be divided into smaller meaningful parts.

What is 'Continuous Data'?

Numerical values obtained through measurement that can take any value within a range, including decimals and fractions (e.g., $160.75\,cm$).

What distinguishes 'Nominal Data' from 'Ordinal Data'?

Nominal Data consists of categories with no natural order (e.g., Blood group), while Ordinal Data consists of categories with a meaningful ranking (e.g., Education level).

What characterizes the 'Interval Scale' of measurement?

Ordered numerical values with equal intervals but no true zero, such as temperature in Celsius where $0^{\circ}C$ does not mean the absence of temperature.

What is the 'Ratio Scale' of measurement?

Ordered numerical values with equal intervals and a true zero, where all arithmetic operations and ratios are meaningful (e.g., $20\,kg$ is twice as much as $10\,kg$).