Data Science in Business

These flashcards cover key concepts and terms related to data science in business, including its applications, performance evaluation metrics, and cognitive aspects affecting decision-making.

Data Science Use Case (DSUC)

A hypothesis to be checked, a question that needs an answer, or a problem that requires a solution in data science.

Machine Learning Canvas (MLC)

A visual tool to help identify data science use cases, showing how a machine learning system transforms predictions into value.

Key Performance Indicators (KPIs)

Quantifiable measures used to track the progress of a business goal or objective.

Cognitive Biases

Inclinations for or against individuals or groups affecting decision-making and judgement, impacting the quality of prediction models.

Operational Analytics

Using real-time data to improve daily business decisions, aiming for immediate actionable insights.

Confusion Matrix

A table used to evaluate the performance of a classification model by comparing predicted values against actual values.

Regression Model

A type of model used to predict a continuous numeric output based on input features.

Data Availability

The accessibility and reliability of data sources required to implement data science use cases.

MLOps (Machine Learning Operations)

The application of DevOps principles to machine learning projects, enhancing collaboration and model deployment.

Churning Customers

Customers who stop using a product or service offered by a company.

Unsupervised Learning

A type of machine learning where models find patterns or structures in data without pre-labeled responses, often used for clustering or dimensionality reduction.

Feature Engineering

The process of using domain knowledge to extract new features from raw data, which can improve the performance of machine learning models.

A/B Testing

A controlled experiment where two or more versions of a variable (e.g., a webpage, an app feature) are compared to determine which one performs better.

Bias-Variance Trade-off

A fundamental concept in machine learning that describes the balance between a model's ability to minimize bias (errors from incorrect assumptions) and variance (errors from sensitivity to small fluctuations in the training data).