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Data Science for Lingerie Design
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Module 1
Foundations for Lingerie Data Analysis
1
Missing Data Imputation for Lingerie Datasets
Classify missing data in a lingerie style rating dataset as MCAR, MAR, or MNAR and select an appropriate imputation strategy.
Outlier Detection in Lingerie Evaluation Data
Detect and handle outlier scores in a multi-rater lingerie evaluation dataset using robust statistical methods (e.g., IQR, Winsorizing).
3
Analyzing Lingerie Design: From Data to Hypotheses
Analyze summary statistics and visualizations for lingerie design attributes (e.g., cup coverage, strap width) to generate initial hypotheses about key style drivers.
4
Cleaning Lingerie Data: Missing Values & Outliers
Solve a numerical problem involving cleaning a small lingerie rating dataset with both missing values and outliers, justifying each decision.
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Identifying Trends and Style Relationships