Clarivate Data Scientist Interview Questions and Answers

180 mins of online test with camera ON. Major topics include Excel, Aptitude, Python, Statistics and Case Study

Q1. Explain Apriori Method
Ans. 
Apriori method is a popular algorithm for frequent itemset mining in data mining.
• Used for finding frequent itemsets in transactional databases

• Based on the concept of association rule mining

• Involves generating candidate itemsets and pruning based on support threshold

• Example: If {milk, bread} is a frequent itemset, then {milk} and {bread} are also frequent
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Q2. Explain train-test in Scikit learn
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Train-test split is a method used to divide a dataset into training and testing sets for model evaluation in Scikit learn.
• Split the dataset into two subsets: training set and testing set

• Training set is used to train the model, while testing set is used to evaluate the model's performance

• Common split ratios are 70-30 or 80-20 for training and testing sets

• Example: X_train, X_test, y_train, y_test = train_test_split(X, y,
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Q1. Explain about projects in current company
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Q2. Why do you want to move to an individual contributor role from a managerial position
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Q1. Why do you want to join Wolters Kluwer?
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Q2. Discussion around analytics and managerment reporting deliverables in current org.
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Q1. Tell me about your self
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Q1. Normal Data Science questions
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Q1. Salary discussion and HR related queries
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Explain dynamic programming with memoization

Q1. Where are you from, and why are you joining the company
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Q2. Why are you joining the company
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Q1. Ask 4 programming question 1)prime in range ,2)Matrix multiplication,3)Spiral Matrix 4)separte vowels from string
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Q1. Explain difference between Faster-RCNN and Yolo v3.
Ans. 
Faster-RCNN and Yolo v3 are both object detection algorithms, but differ in their approach and performance.
• Faster-RCNN uses a two-stage approach, first generating region proposals and then classifying them.

• Yolo v3 uses a single-stage approach, directly predicting bounding boxes and class probabilities.

• Faster-RCNN is generally more accurate but slower, while Yolo v3 is faster but less accurate.

• Faster-RCNN is better suit...read more
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Q2. How RNN handles exploding/vanishing Gradient?
Ans. 
RNN uses techniques like gradient clipping, weight initialization, and LSTM/GRU cells to handle exploding/vanishing gradients.
• Gradient clipping limits the magnitude of gradients during backpropagation.

• Weight initialization techniques like Xavier initialization help in preventing vanishing gradients.

• LSTM/GRU cells have gating mechanisms that allow the network to selectively remember or forget information.

• Batch normaliza...read more
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