Data Patterns Flashcards
What are data patterns, and why are they important?
Data patterns are recurring structures, trends, or regularities in data. They help in making predictions, extracting insights, and understanding relationships between variables.
What is trend analysis in data patterns?
Trend analysis involves detecting and forecasting trends over time, such as the decline of physical storage devices or the rise of short-form videos.
What is seasonality, and how does it differ from cyclic patterns?
Seasonality refers to predictable patterns that occur at regular time intervals (e.g., increased shopping activity during Christmas), while cyclic patterns occur at irregular intervals based on economic or external factors.
What is clustering, and where is it used?
Clustering groups similar data points together. It is used in segmentation tasks, such as grouping students based on academic scores or classifying diseases into subcategories.
What is correlation and association in data science?
Correlation measures the relationship between two variables, while association determines how frequently items appear together in data, like in market basket analysis.
What is anomaly detection, and why is it important?
Anomaly detection identifies irregularities or outliers in data. It is crucial for fraud detection, cybersecurity, and quality control.
How does sequential pattern analysis help in business?
It helps predict customer behavior, such as identifying that a customer who buys a laptop is likely to purchase a mouse or a bag afterward.
Give an example of a cyclic pattern in finance.
The stock market may experience a year-end rally in December and a drop in January, known as the “January effect.”
Why is identifying data patterns essential for business decision-making?
Recognizing patterns allows businesses to optimize strategies, predict customer behavior, and improve operational efficiency.
How do data patterns contribute to predictive analytics?
By analyzing past trends and patterns, businesses can forecast future behaviors, such as predicting consumer demand or financial market movements.
