Data Science Foundations

Flashcards covering key vocabulary and concepts in Data Science Foundations.

Data

Raw facts, measurements, or observations used to draw conclusions or make decisions.

Quantitative Data

Numerical measurements.

Qualitative Data

Descriptive, non-numerical observations.

Textual Data

Documents, tweets, articles.

Numerical Data

Sensor readings, sales figures.

Categorical Data

Labels like gender, product category, or timing.

Multimedia Data

Images, audio, video.

Time-Series Data

Data collected over time, such as stock prices or weather.

Data Collection

The process of gathering and cleaning raw data.

Exploratory Data Analysis (EDA)

Visualizing and summarizing data to find patterns.

Machine Learning

Building models to predict or explain phenomena using data.

Artificial Intelligence (AI)

Uses data science methodologies to create models that mimic human intelligence.

Data Science Workflow

A systematic process from problem definition to deployment of solutions.

Model Evaluation

Using metrics like accuracy and precision to assess model performance.

Privacy Regulations

Laws governing data protection, like GDPR and CCPA.

Bias in Algorithms

Unintended discrimination in model outcomes due to unrepresentative training data.

Feature Engineering

Creating or transforming variables to improve model performance.

Deployment

Integrating the model into real-world applications.

Collaborative Research

Partnership between domain experts and data scientists to solve problems.

Ethics in Data Science

Considerations regarding data usage, consent, and privacy.