Data Science Fundamentals

Data that represents measurements, numbers, or quantities.

Data describing qualities or characteristics that cannot be measured numerically.

Data consisting of images, often represented as matrices of pixels.

Data composed of written words, sentences, and paragraphs, often used to gain insights from text.

Data that includes location information, often used for mapping, navigation, and analysis of geographic patterns.

Data representing relationships and connections between entities, often visualized as networks with nodes and edges.

Data that comes from various sources, not all of which are publicly available. Researchers often use this to understand patterns and trends.

Understanding data types is crucial in data science because it determines how data can be stored, visualized, and analyzed. Different data types require different techniques and tools.

Storing data on servers owned and managed by another company.

A type of database designed for storing unstructured data, like emails, videos, and social media posts.

A type of database designed for storing structured data in tables, like data in spreadsheets.

The language used to interact with Relational Databases.

The language used to interact with Document Databases.

The process of retrieving specific data from a database.

The process of choosing a location to store your data, either on-premises or in the cloud.

The process of choosing the appropriate type of database based on the type of data you are storing.

A metric used to measure customer loyalty and predict future business growth by asking how likely customers are to recommend a company to others on a scale of 0 to 10.

A collection of interconnected computers that work together to store and process large amounts of data, often used by businesses for data storage and management.

The process of securely storing and retrieving data in a way that is efficient and accessible for analysis and usage.

A structured set of steps used to collect, clean, analyze, and interpret data to extract insights and solve problems.

Storing data across multiple computers or servers to ensure data availability and scalability.

Storing data in a way that allows for fast and efficient access, regardless of the data's size or location.

Data collected from various sources like web events, surveys, customer information, logistics records, and financial transactions.

Data like page URLs, click identifiers, timestamps, and user IDs, allowing companies to track website user behavior and conversion rates.

Data that is freely available for anyone to use, share, and build upon, often collected and shared by organizations and institutions.

The process of collecting data from individuals or groups through interviews, questionnaires, or focus groups, providing insights into their opinions, preferences, and behaviors.

The process of organizing and storing data from various sources for efficient access and analysis. This can involve using data warehouses, data lakes, or other storage solutions based on the data's nature and size.

A series of steps that automate the process of collecting, storing, transforming, and analyzing data, often involving multiple tools and technologies for efficient data flow.

The first step in the data science workflow, involving collecting data from various sources, such as company databases, open data repositories, or surveys.

Involves organizing and preparing data for analysis and visualization, like merging data sources or converting data structures to fit database schemas.

The process of taking data from a source and preparing it for analysis.

The first step in the ETL process where raw data is extracted from its source.

The final step in the ETL process, where prepared data is loaded into a database or other storage.

The process of automating the data pipeline steps, ensuring that tasks are executed regularly and efficiently.

A popular tool used for automating and orchestrating complex data pipelines.

Data engineers design and build custom data pipelines for projects.