Data Science Interview questions

1. Pandas

Sales Data Analysis: A retail company collects daily sales data across multiple stores. Using Pandas, a data analyst reads the CSV file into a DataFrame, cleans the data by handling missing values, and then summarizes sales by region and product category. The analyst can easily filter and group data to uncover trends, such as identifying best-selling products and seasonal variations.

2. NumPy

Statistical Analysis: A sports analytics team uses NumPy to analyze player performance metrics. By creating NumPy arrays from the data, they perform statistical computations, such as calculating the mean, median, and standard deviation of player scores across multiple games. This analysis helps in assessing player consistency and making data-driven decisions for team selection.

3.Matplotlib

Visualizing Financial Trends: A financial analyst uses Matplotlib to create visualizations of stock price trends over time. By plotting line graphs of stock prices alongside trading volume, the analyst can visualize correlations and identify patterns, such as price spikes or drops during significant market events. These visualizations are crucial for presentations to stakeholders.

4. Seaborn

Exploratory Data Analysis (EDA): A data scientist conducts an exploratory analysis on a dataset containing customer demographics and purchase history. Using Seaborn, they create a pairplot to visualize relationships between various attributes, such as age, income, and spending score. This visual analysis helps identify customer segments and informs targeted marketing strategies.

5. Scikit-learn

Predictive Modeling for Customer Churn: A telecommunications company uses Scikit-learn to build a predictive model to identify customers likely to churn. By preprocessing the data (handling missing values and encoding categorical features), the data scientist applies algorithms like logistic regression and random forests. After training and validating the model, they use it to develop proactive retention strategies for at-risk customers.