L1: What is Data Science?

A set of step-by-step instructions to solve a problem or complete a task. Algorithms are fundamental in computer science and programming.

A representation of the relationships and patterns found in data to make predictions or analyze complex systems retaining essential elements needed for analysis. Models can vary in complexity and are used in various fields such as statistics, machine learning, and simulation.

When a data point or points occur significantly outside of most of the other data in a data set, potentially indicating anomalies, errors, or unique phenomena that could impact statistical analysis or modeling. Identifying outliers is critical for accurate data interpretation.

A systematic approach using mathematical and statistical analysis is used to interpret numerical data. Quantitative analysis is often used in economics, finance, and scientific research.

Data is organized and formatted into a predictable schema, usually related tables with rows and columns. Examples include databases and spreadsheets.

Unorganized data that lacks a predefined data model or organization makes it harder to analyze using traditional methods. This data type often includes text, images, videos, and other content that doesn’t fit neatly into rows and columns like structured data.

True Structured data follows a clear format, making it more straightforward for data analysis tools.

Quantitative This type of analysis is essential in fields that rely on data-driven decision-making.