Introduction to Data Science

Practice flashcards covering key concepts, applications, and tools in the field of Data Science as taught in the course.

What is Data Science?

A multidisciplinary field integrating mathematics, statistics, machine learning, and computer science to extract meaningful insights from data.

What is the CRISP-DM framework?

A systematic process for solving data science problems that includes business understanding, data understanding, data preparation, data modeling, model evaluation, and model deployment.

What are key application areas of data science in business?

Customer behavior analysis, market basket analysis, sales forecasting, personalized marketing, and churn prediction.

How does data science benefit healthcare?

Improves patient care, operational efficiency, and medical research outcomes.

What are some tools used in data science?

Microsoft Excel, Python, and R.

What are some key applications of data science in finance?

Credit scoring, fraud detection, risk assessment, and algorithmic trading.

What is the role of Microsoft Excel in data science?

Data storage, organization, analysis, and visualization.

What does data preparation involve in the CRISP-DM framework?

Data preprocessing, including transformation, aggregation, filling missing data, and creating new derived attributes.

What is the impact of data-driven decision-making?

Enhances organizational performance, customer satisfaction, and societal outcomes.

What is the significance of model evaluation in data science?

Assessing the effectiveness of predictive models to ensure they successfully solve business problems.