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Data science is a fast-emerging field with numerous opportunities. If one is preparing for an interview in data science, then he/she has to be comfortable with both the theoretical aspects and practical applications of the subject. Here's a glance at some of the most frequently asked questions in a data science interview along with their elaborated answers.
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DATA SCIENCE Top 15 Data Science Interview Questions and Answers (2024)
1. What is Data Science? Answer : Data Science is a field that includes domain expertise, programming, and the basics of mathematics and statistics to derive valuable insights from data. The data collection and cleaning process ends with exploration and analysis. https://nareshit.com/courses/data-science-online-training
03 2. What is the difference between Supervised and Unsupervised Learning? Answer: In supervised learning, models are trained based on labeled data. The output is known in a supervised setting. Whereas in case of unsupervised learning, unlabeled data is used to find patterns or relationships, without any explicit output labels. https://nareshit.com/courses/data-science-online-training
3. What is Overfitting, and how to avoid it? Answer : Overfitting happens when the model learns the noise as well as the signal from the training data; thereby, it performs well over the training set but bad over the testing set. Prevention Techniques: Cross-validation, pruning, regularization (L1/L2), and using less complex models. https://nareshit.com/courses/data-science-online-training
4. What is Cross-Validation? Why does it matter? Answer: Cross-validation is an evaluation method for models by splitting the dataset into subsets, where a model is trained on some subsets and the model is evaluated on others. It enables generalization of the model and keeps it free from overfitting. https://nareshit.com/courses/data-science-online-training
5. What is bias-variance trade-off? Answer : The trade-off between bias and variance should be maintained to avoid over generalization of the complexity of the model. Higher bias tends towards overfitting, while high variance indicates underfitting. Again, the goal here is to have an optimum level to avoid poor model performance. https://nareshit.com/courses/data-science-online-training
6. Define Confusion Matrix and what does it consist of? Answer: A Confusion Matrix is a matrix for assessing the quality of the classification model. Following are constituents ; True Positive (TP): Correctly classified positive cases. True Negative (TN): Correctly classified negative cases. False Positive (FP): Those cases which were predicted to be positive but are actually negative False Negative (FN): Those cases which were predicted to be negative but are actually positive https://nareshit.com/courses/data-science-online-training
7. What is Precision and Recall? Answer : Precision: Number of true positives out of number of all true positives predicted. Recall: Number of true positives out of number of total actual positives. Precision = TP / (TP + FP) Recall = TP / (TP + FN) https://nareshit.com/courses/data-science-online-training
8. What is the difference between a Z-score and a P-value? Answer : Z-score measures the number of standard deviations a data point is from the mean. P-value helps decide how strongly a test can support a claim about population and how likely is the result to have occurred by chance. https://nareshit.com/courses/data-science-online-training
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