PwC Data Scientist Interview Questions and Answers for Experienced

Q1. Confusion Matrix, RoC curve
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Simple aptitude test with mcqs. No stress

Q1. ML, DL, Data Science, Python
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Q1. AI,ML, Data Science, Use cases
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1 hour test with 3 python programming questions.

Q1. Are all the decision trees same in a random forest ?
Ans. 
No, decision trees in a random forest are different due to the use of bootstrapping and feature randomization.
• Decision trees in a random forest are trained on different subsets of the data through bootstrapping.

• Each decision tree in a random forest also considers only a random subset of features at each split.

• The final prediction in a random forest is made by aggregating the predictions of all individual decision trees
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Q2. How will you handle class imbalanced dataset to increase the f1 score ?
Ans. 
Handling class imbalanced dataset involves techniques like resampling, using different algorithms, adjusting class weights, and using ensemble methods.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the...read more
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Q1. Python programming.(List) SQL. Statistics.(Basics like CLT and Hypothesis Testing).
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Q2. Question related to time series analysis. Program to detect outlier in time series.
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Q1. Project Description and basic concepts related to data science and machine learning.
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Q2. Describe project u have done. Which algorithm is your favourite?
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Q1. What is the difference between precision and recall
Ans. 
Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.
• Precision focuses on the accuracy of positive predictions, while recall focuses on the proportion of actual positives that were correctly identified.

• Precision = TP / (TP + FP), Recall = TP / (TP + FN)

• High prec...read more
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