Coding Patterns for Data Science Role

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While coding patterns in the context of a data science role may not be as standardized as in algorithmic coding interviews, there are certain coding practices and techniques that are commonly used in the field of data science. Here’s a list of coding patterns and practices that are relevant to a data science role:

1. Data Cleaning and Preprocessing:

Pattern: Handling missing values, outlier detection, and data imputation

Techniques:

• DataFrame manipulation
• Data cleaning and preprocessing
• GroupBy operations
• Imputing missing values with mean, median, or mode
• Detecting and handling outliers
• Normalizing and scaling numerical features

2. Feature Engineering:

Pattern: Creating new features to enhance model performance

Techniques:

• Feature scaling
• Handling missing data
• Creating interaction terms
• Encoding categorical variables
• Extracting features from text or date fields

3. Exploratory Data Analysis (EDA):