Data Science Midterm Exam

The process of searching through a large dataset to find meaningful patterns, trends, and insights.

A massive collection of raw data that is collected, stored, and analyzed to improve decision-making.

Data that is difficult to analyze using traditional methods because of its variety and complexity. Examples include text documents, images, and videos.

The accuracy and reliability of the data. Is the information truthful and trustworthy?

A field that uses data and statistics to extract knowledge from large datasets and improve decision-making.

A data analysis technique used to group similar items together, like customers with similar purchasing behaviors.

A technique to find relationships between items often bought together, like bread and butter.

The ability to use insights derived from data to take action or make informed decisions.

The process of converting unstructured or raw data into a structured format for easier analysis and organization in a database.

It refers to the vast amount of data that is collected and processed.

The process of extracting data from source systems, transforming it into a consistent format, and loading it into a target data store.

The final stage of data analysis where insights are presented and actions are taken based on the findings.

A collection of data that is organized and structured in a way that allows for easy access and analysis.

The process of creating a table or matrix representing the data, where rows usually represent individual data points and columns represent the attributes or features of those data points.

A structured and iterative approach to data science projects, involving steps from defining the problem to deploying and monitoring the solution. It helps guide the data scientist through the entire project lifecycle.

A type of predictive modeling that aims to predict a continuous outcome, such as sales figures or stock prices.

A document that provides a comprehensive overview of the entire data science project, including the goals, objectives, scope, and methodology. It serves as a reference point throughout the project and evolves as new information emerges.

A document that provides detailed information about the data used in a data science project. It includes data field descriptions, relationships between tables, data types, and any relevant constraints.

The process of gathering and preparing the data for analysis. It can include extracting data from multiple sources, cleaning and transforming data, and ensuring consistency and quality.

A representation of the data pipeline used in a data science project. It shows how data flows through different stages of the process, including data acquisition, cleaning, transformation, and modeling.

A widely used software for storing and integrating data in a traditional way.

A user-friendly system for data analysis and reporting. It makes working with large amounts of data easier and more efficient.

The initial step in setting up the R programming environment.

The area where visual representations like graphs created during data exploration are displayed. You can also find packages for R, and access its official documentation.

The space in the R environment where you write and execute code.

This area shows the elements you've added to R, such as datasets, variables, functions, and more. It confirms whether your data has been loaded correctly.

A specific type of database system that utilizes a structured table-based approach to organize and manage data.

A decision support system that integrates, aggregates, and reports data, offering analysis capabilities.

The class() function in R Programming reveals the kind of object. It provides high-level information about the object's type and structure.

The typeof() function in R Programming determines the basic data type of an object. It focuses on the low-level data structure, like integer or character.

The length() function in R Programming determines the number of elements in a vector-like object. It tells you how many individual components are there.

The attributes() function in R Programming provides additional information about an object, such as its name, dimension, and other metadata. Metadata is additional information about the object, for example, the data's creation date.

In R's data manipulation, the apply() function transforms and recalculates data based on a specified function. Think of it like running a calculation on all the data in a certain way.

In R's data manipulation, the combine() function merges and combines related data into one structure. Think of it as bringing together separate pieces of information into a cohesive whole.

Dividing large datasets into smaller parts or selecting specific parts based on conditions.

Applying transformations and calculations to data, like changing values or creating new ones.

Combining datasets together or grouping data based on specific characteristics.

A powerful tool with a user-friendly interface for coding, analyzing, and visualizing data.

A basic, command-line interface for interacting with R, but lacks advanced features like a code editor.

Displays the results of your code, like text output or calculated values, and allows direct command input.

Provides a space to write, save, and run your R code, making it more organized and manageable.

Shows all the data, variables, and functions you've created or loaded into your R session.