?How do you handle missing data in a dataset, and why is it important

Handling missing data is crucial to ensure the reliability of a model. Common techniques include: Removing Rows/Columns: If the missing data is minimal and won't impact results significantly. Imputation: Using mean, median, or mode for numerical data; or frequent categories for categorical data. Advanced Methods: Such as using predictive modeling (e.g., regression or KNN) to fill gaps. These steps prevent biased results and maintain the dataset's integrity for analysis.

?How do you explain a machine learning model to a non-technical stakeholder

Focus on simplicity and outcomes. Explain the Purpose: Highlight what the model does (e.g., predicting customer churn). Simplify the Methodology: Use relatable analogies (e.g., "It's like a teacher learning from past tests to predict future answers"). Emphasize Results: Share clear metrics or visualizations (e.g., "The model predicts churn with 85% accuracy"). This approach bridges technical details and business understanding.

Data science mock interview basics

What will you learn in this course?

Develop effective Python programming skills for data analysis and machine learning interview questions
Construct optimized SQL queries to solve real-world data retrieval and manipulation challenges

Apply statistical concepts, probability, and machine learning techniques to analyze data and evaluate models
Demonstrate strong problem-solving, communication, and behavioral interview strategies for data science roles

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Lessons | 3

00:45:41

Data Science Interview Mock Data Science Interview question for freshers

00:46:12

Data Science Interview Mock Data Science Interview Questions Data Science Interview Experience

00:50:20

Data Science Mock Interview with Mani Data Science Interview question and answer

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Data science mock interview course, in this course designed to help you excel in data science interviews by simulating real-world scenarios and questions. Learn to handle technical challenges like Python programming, SQL queries, data analysis, and visualization. Master statistical concepts, probability, and machine learning techniques, including model evaluation and optimization. Practice scenario-based problem-solving and gain insights into structuring your answers effectively for behavioral and technical questions. The course includes mock interview sessions with feedback, equipping you with the confidence and skills to showcase your expertise. Perfect for beginners and seasoned professionals looking to refine their interview performance and land their dream data science role.

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